IPTranslator: Facilitating Patent Search with Machine Translation

Abstract: Intellectual Property professionals frequently need to carry out patent searches for a variety of reasons. During a typical search, they will retrieve approximately 30% of their results in a foreign language. The machine translation (MT) options currently available to patent searchers for these foreign-language patents vary in their quality, consistency, and general level of service. In this article, we introduce IPTranslator; an MT web service designed to cater for the needs of patent searchers. At the core of IPTranslator is a set of MT systems developed specifically for translating patent text. We describe the challenges faced in adapting MT technology to such a complex domain, and how the systems were evaluated to ensure that the quality was fit for purpose. Finally, we present the framework through which the IPTranslator service is delivered to users, and the value-adding features which address many of the issues with existing solutions.

patent search
machine translation
iptranslator


GrawlTCQ: Terminology and Corpora Building by Ranking Simultaneously Terms, Queries and Documents using Graph Random Walks

Abstract: In this paper, we present GrawlTCQ, a new bootstrapping algorithm for building specialized terminology, corpora and queries, based on a graph model. We model links between documents, terms and queries, and use a random walk with restart algorithm to compute relevance propagation. We have evaluated GrawlTCQ on an AFP English corpus of 57,441 news over 10 categories. For corpora building, GrawlTCQ outperforms the BootCaT tool, which is vastly used in the domain. For 1,000 documents retrieved, we improve mean precision by 25%. GrawlTCQ has also shown to be faster and more robust than BootCaT over iterations.


Evaluating Factuality in Generation with Dependency-level Entailment

Abstract: Despite significant progress in text generation models, a serious limitation is their tendency to produce text that is factually inconsistent with information in the input. Recent work has studied whether textual entailment systems can be used to identify factual errors; however, these sentence-level entailment models are trained to solve a different problem than generation filtering and they do not localize which part of a generation is non-factual. In this paper, we propose a new formulation of entailment that decomposes it at the level of dependency arcs. Rather than focusing on aggregate decisions, we instead ask whether the semantic relationship manifested by individual dependency arcs in the generated output is supported by the input. Human judgments on this task are difficult to obtain; we therefore propose a method to automatically create data based on existing entailment or paraphrase corpora. Experiments show that our dependency arc entailment model trained on this data can identify factual inconsistencies in paraphrasing and summarization better than sentence-level methods or those based on question generation, while additionally localizing the erroneous parts of the generation.

dependency-level entailment
factual error
dependency arc


Core Semantic First: A Top-down Approach for AMR Parsing

Abstract: We introduce a novel scheme for parsing a piece of text into its Abstract Meaning Representation (AMR): Graph Spanning based Parsing (GSP). One novel characteristic of GSP is that it constructs a parse graph incrementally in a top-down fashion. Starting from the root, at each step, a new node and its connections to existing nodes will be jointly predicted. The output graph spans the nodes by the distance to the root, following the intuition of first grasping the main ideas then digging into more details. The core semantic first principle emphasizes capturing the main ideas of a sentence, which is of great interest. We evaluate our model on the latest AMR sembank and achieve the state-of-the-art performance in the sense that no heuristic graph re-categorization is adopted. More importantly, the experiments show that our parser is especially good at obtaining the core semantics.


PCFGs with Syntactic and Prosodic Indicators of Speech Repairs

Abstract: A grammatical method of combining two kinds of speech repair cues is presented. One cue, prosodic disjuncture, is detected by a decision tree-based ensemble classifier that uses acoustic cues to identify where normal prosody seems to be interrupted (Lickley, 1996). The other cue, syntactic parallelism, codifies the expectation that repairs continue a syntactic category that was left unfinished in the reparandum (Levelt, 1983). The two cues are combined in a Treebank PCFG whose states are split using a few simple tree transformations. Parsing performance on the Switchboard and Fisher corpora suggests that these two cues help to locate speech repairs in a synergistic way.


Coordinate Structure Analysis with Global Structural Constraints and Alignment-Based Local Features

Abstract: We propose a hybrid approach to coordinate structure analysis that combines a simple grammar to ensure consistent global structure of coordinations in a sentence, and features based on sequence alignment to capture local symmetry of conjuncts. The weight of the alignment-based features, which in turn determines the score of coordinate structures, is optimized by perceptron training on a given corpus. A bottom-up chart parsing algorithm efficiently finds the best scoring structure, taking both nested or non-overlapping flat coordinations into account. We demonstrate that our approach outperforms existing parsers in coordination scope detection on the Genia corpus.

alignment-based local feature
sequence alignment
local symmetry
coordination
dual decomposition


Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference

Abstract: The task of ultra-fine entity typing (UFET) seeks to predict diverse and free-form words or phrases that describe the appropriate types of entities mentioned in sentences. A key challenge for this task lies in the large number of types and the scarcity of annotated data per type. Existing systems formulate the task as a multi-way classification problem and train directly or distantly supervised classifiers. This causes two issues: (i) the classifiers do not capture the type semantics because types are often converted into indices; (ii) systems developed in this way are limited to predicting within a pre-defined type set, and often fall short of generalizing to types that are rarely seen or unseen in training. This work presents LITE🍻, a new approach that formulates entity typing as a natural language inference (NLI) problem, making use of (i) the indirect supervision from NLI to infer type information meaningfully represented as textual hypotheses and alleviate the data scarcity issue, as well as (ii) a learning-to-rank objective to avoid the pre-defining of a type set. Experiments show that, with limited training data, LITE obtains state-of-the-art performance on the UFET task. In addition, LITE demonstrates its strong generalizability by not only yielding best results on other fine-grained entity typing benchmarks, more importantly, a pre-trained LITE system works well on new data containing unseen types.1

indirect supervision
natural language inference
ultra-fine entity typing


Understanding Dataset Design Choices for Multi-hop Reasoning

Abstract: Learning multi-hop reasoning has been a key challenge for reading comprehension models, leading to the design of datasets that explicitly focus on it. Ideally, a model should not be able to perform well on a multi-hop question answering task without doing multi-hop reasoning. In this paper, we investigate two recently proposed datasets, WikiHop and HotpotQA. First, we explore sentence-factored models for these tasks; by design, these models cannot do multi-hop reasoning, but they are still able to solve a large number of examples in both datasets. Furthermore, we find spurious correlations in the unmasked version of WikiHop, which make it easy to achieve high performance considering only the questions and answers. Finally, we investigate one key difference between these datasets, namely span-based vs. multiple-choice formulations of the QA task. Multiple-choice versions of both datasets can be easily gamed, and two models we examine only marginally exceed a baseline in this setting. Overall, while these datasets are useful testbeds, high-performing models may not be learning as much multi-hop reasoning as previously thought.

multi-hop reasoning
reasoning
hotpotqa
large number


SemEval-2013 Task 11: Word Sense Induction and Disambiguation within an End-User Application

Abstract: In this paper we describe our Semeval-2013 task on Word Sense Induction and Disambiguation within an end-user application, namely Web search result clustering and diversification. Given a target query, induction and disambiguation systems are requested to cluster and diversify the search results returned by a search engine for that query. The task enables the end-to-end evaluation and comparison of systems.

disambiguation
end-user application
semeval-2013 task


A double-blind experiment on interannotator agreement: the case of dependency syntax and Finnish

Abstract: Manually performed treebanking is an expensive effort compared with automatic annotation. In return, manual treebanking is generally believed to provide higherquality/value syntactic annotation than automatic methods. Unfortunately, there is little or no empirical evidence for or against this belief, though arguments have been voiced for the high degree of subjectivity in other levels of linguistic analysis (e.g. morphological annotation). We report a double-blind annotation experiment at the level of dependency syntax, using a small Finnish corpus as the analysis data. The results suggest that an interannotator agreement can be reached as a result of reviews and negotiations that is much higher than the corresponding labelled attachment scores (LAS) reported for stateof-the-art dependency parsers.

double-blind experiment
interannotator agreement
dependency syntax


An Improved Extraction Pattern Representation Model for Automatic IE Pattern Acquisition

Abstract: Several approaches have been described for the automatic unsupervised acquisition of patterns for information extraction. Each approach is based on a particular model for the patterns to be acquired, such as a predicate-argument structure or a dependency chain. The effect of these alternative models has not been previously studied. In this paper, we compare the prior models and introduce a new model, the Subtree model, based on arbitrary subtrees of dependency trees. We describe a discovery procedure for this model and demonstrate experimentally an improvement in recall using Subtree patterns.

event-specific document
extractor
common word pattern


Medical text simplification using synonym replacement: Adapting assessment of word difficulty to a compounding language

Abstract: Medical texts can be difficult to understand for laymen, due to a frequent occurrence of specialised medical terms. Replacing these difficult terms with easier synonyms can, however, lead to improved readability. In this study, we have adapted a method for assessing difficulty of words to make it more suitable to medical Swedish. The difficulty of a word was assessed not only by measuring the frequency of the word in a general corpus, but also by measuring the frequency of substrings of words, thereby adapting the method to the compounding nature of Swedish. All words having a MeSH synonym that was assessed as easier, were replaced in a corpus of medical text. According to the readability measure LIX, the replacement resulted in a slightly more difficult text, while the readability increased according to the OVIX measure and to a preliminary reader study.

simplification
synonym replacement
medical swedish
medical text


Rule Markov Models for Fast Tree-to-String Translation

Abstract: Most statistical machine translation systems rely on composed rules (rules that can be formed out of smaller rules in the grammar). Though this practice improves translation by weakening independence assumptions in the translation model, it nevertheless results in huge, redundant grammars, making both training and decoding inefficient. Here, we take the opposite approach, where we only use minimal rules (those that cannot be formed out of other rules), and instead rely on a rule Markov model of the derivation history to capture dependencies between minimal rules. Large-scale experiments on a state-of-the-art tree-to-string translation system show that our approach leads to a slimmer model, a faster decoder, yet the same translation quality (measured using B) as composed rules.

translation model
rule markov model
fast decoder


Situation Entity Annotation

Abstract: This paper presents an annotation scheme for a new semantic annotation task with relevance for analysis and computation at both the clause level and the discourse level. More specifically, we label the finite clauses of texts with the type of situation entity (e.g., eventualities, statements about kinds, or statements of belief) they introduce to the discourse, following and extending work by Smith (2003). We take a feature-driven approach to annotation, with the result that each clause is also annotated with fundamental aspectual class, whether the main NP referent is specific or generic, and whether the situation evoked is episodic or habitual. This annotation is performed (so far) on three sections of the MASC corpus, with each clause labeled by at least two annotators. In this paper we present the annotation scheme, statistics of the corpus in its current version, and analyses of both inter-annotator agreement and intra-annotator consistency.

annotation scheme
clause
situation entity


EDITOR: An Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints

Abstract: We introduce an Edit-Based TransfOrmer with Repositioning (EDITOR), which makes sequence generation flexible by seamlessly allowing users to specify preferences in output lexical choice. Building on recent models for non-autoregressive sequence generation (Gu et al., 2019), EDITOR generates new sequences by iteratively editing hypotheses. It relies on a novel reposition operation designed to disentangle lexical choice from word positioning decisions, while enabling efficient oracles for imitation learning and parallel edits at decoding time. Empirically, EDITOR uses soft lexical constraints more effectively than the Levenshtein Transformer (Gu et al., 2019) while speeding up decoding dramatically compared to constrained beam search (Post and Vilar, 2018). EDITOR also achieves comparable or better translation quality with faster decoding speed than the Levenshtein Transformer on standard Romanian-English, English-German, and English-Japanese machine translation tasks.

edit-based transformer
lexical constraint
editor


Improving machine translation by training against an automatic semantic frame based evaluation metric

Abstract: We present the first ever results showing that tuning a machine translation system against a semantic frame based objective function, MEANT, produces more robustly adequate translations than tuning against BLEU or TER as measured across commonly used metrics and human subjective evaluation. Moreover, for informal web forum data, human evaluators preferred MEANT-tuned systems over BLEU- or TER-tuned systems by a significantly wider margin than that for formal newswire—even though automatic semantic parsing might be expected to fare worse on informal language. We argue that by preserving the meaning of the translations as captured by semantic frames right in the training process, an MT system is constrained to make more accurate choices of both lexical and reordering rules. As a result, MT systems tuned against semantic frame based MT evaluation metrics produce output that is more adequate. Tuning a machine translation system against a semantic frame based objective function is independent of the translation model paradigm, so, any translation model can benefit from the semantic knowledge incorporated to improve translation adequacy through our approach.

machine translation
semantic frame
evaluation metric


KYOTO: a System for Mining, Structuring and Distributing Knowledge across Languages and Cultures

Abstract: We outline work performed within the framework of a current EC project. The goal is to construct a language-independent information system for a specific domain (environment/ecology/biodiversity) anchored in a language-independent ontology that is linked to wordnets in seven languages. For each language, information extraction and identification of lexicalized concepts with ontological entries is carried out by text miners (Kybots). The mapping of language-specific lexemes to the ontology allows for crosslinguistic identification and translation of equivalent terms. The infrastructure developed within this project enables long-range knowledge sharing and transfer across many languages and cultures, addressing the need for global and uniform transition of knowledge beyond the specific domains addressed here.

culture
project
specific domain
information extraction
kyoto


Joint Transition-based Dependency Parsing and Disfluency Detection for Automatic Speech Recognition Texts

Abstract: Joint dependency parsing with disﬂuency detection is an important task in speech language processing. Recent methods show high performance for this task, although most authors make the unrealistic assumption that input texts are transcribed by human annotators. In real-world applications, the input text is typically the output of an automatic speech recognition (ASR) system, which implies that the text contains not only disﬂuency noises but also recognition errors from the ASR system. In this work, we propose a parsing method that handles both disﬂuency and ASR errors us-ing an incremental shift-reduce algorithm with several novel features suited to ASR output texts. Because the gold dependency information is usually annotated only on transcribed texts, we also introduce an alignment-based method for transferring the gold dependency annotation to the ASR output texts to construct training data for our parser. We conducted an experiment on the Switchboard corpus and show that our method outperforms conventional methods in terms of dependency parsing and disﬂuency detection.

disfluency detection
asr output text
transition-based dependency parser


The NRC System for Discriminating Similar Languages

Abstract: We describe the system built by the National Research Council Canada for the ”Discriminating between similar languages” (DSL) shared task. Our system uses various statistical classifiers and makes predictions based on a two-stage process: we first predict the language group, then discriminate between languages or variants within the group. Language groups are predicted using a generative classifier with 99.99% accuracy on the five target groups. Within each group (except English), we use a voting combination of discriminative classifiers trained on a variety of feature spaces, achieving an average accuracy of 95.71%, with per-group accuracy between 90.95% and 100% depending on the group. This approach turns out to reach the best performance among all systems submitted to the open and closed tasks.

dsl
language group
two-step classification approach
submission track
good result


Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation

Abstract: Natural language generation (NLG) spans a broad range of tasks, each of which serves for specific objectives and desires different properties of generated text. The complexity makes automatic evaluation of NLG particularly challenging. Previous work has typically focused on a single task and developed individual evaluation metrics based on specific intuitions. In this paper, we propose a unifying perspective based on the nature of information change in NLG tasks, including compression (e.g., summarization), transduction (e.g., text rewriting), and creation (e.g., dialog). _Information alignment_ between input, context, and output text plays a common central role in characterizing the generation. With automatic alignment prediction models, we develop a family of interpretable metrics that are suitable for evaluating key aspects of different NLG tasks, often without need of gold reference data. Experiments show the uniformly designed metrics achieve stronger or comparable correlations with human judgement compared to state-of-the-art metrics in each of diverse tasks, including text summarization, style transfer, and knowledge-grounded dialog.


On the Use of Virtual Evidence in Conditional Random Fields

Abstract: Virtual evidence (VE), first introduced by (Pearl, 1988), provides a convenient way of incorporating prior knowledge into Bayesian networks. This work generalizes the use of VE to undirected graphical models and, in particular, to conditional random fields (CRFs). We show that VE can be naturally encoded into a CRF model as potential functions. More importantly, we propose a novel semi-supervised machine learning objective for estimating a CRF model integrated with VE. The objective can be optimized using the Expectation-Maximization algorithm while maintaining the discriminative nature of CRFs. When evaluated on the CLASSIFIEDS data, our approach significantly outperforms the best known solutions reported on this task.

virtual evidence
conditional random field
prior knowledge
function


Incorporating Textual Information on User Behavior for Personality Prediction

Abstract: Several recent studies have shown that textual information of user posts and user behaviors such as liking and sharing the specific posts are useful for predicting the personality of social media users. However, less attention has been paid to the textual information derived from the user behaviors. In this paper, we investigate the effect of textual information on user behaviors for personality prediction. Our experiments on the personality prediction of Twitter users show that the textual information of user behaviors is more useful than the co-occurrence information of the user behaviors. They also show that taking user behaviors into account is crucial for predicting the personality of users who do not post frequently.

textual information
user behavior
personality prediction


Now We Stronger than Ever: African-American English Syntax in Twitter

Abstract: African American English (AAE) is a well-established dialect that exhibits a distinctive syntax, including constructions like habitual be . Using data mined from the social media service Twitter, the proposed senior thesis project intends to study the demographic distribution of a sub-set of AAE syntactic constructions. This study expands on previous sociolinguistic Twitter work (Eisenstein et al., 2011) by adding part-of-speech tags to the data, thus enabling detection of short-range syntactic features. Through an analysis of ethnic and gender data associated with AAE tweets, this project will provide a more accurate description of the dialect’s speakers and distribution.

african-american english
twitter
dialect


SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images

Abstract: We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems. The task focused on memes and had three subtasks: (i) detecting the techniques in the text, (ii) detecting the text spans where the techniques are used, and (iii) detecting techniques in the entire meme, i.e., both in the text and in the image. It was a popular task, attracting 71 registrations, and 22 teams that eventually made an official submission on the test set. The evaluation results for the third subtask confirmed the importance of both modalities, the text and the image. Moreover, some teams reported benefits when not just combining the two modalities, e.g., by using early or late fusion, but rather modeling the interaction between them in a joint model.

persuasion techniques
texts
semeval-2021 task


Parallel Iterative Edit Models for Local Sequence Transduction

Abstract: We present a Parallel Iterative Edit (PIE) model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC). Recent approaches are based on the popular encoder-decoder (ED) model for sequence to sequence learning. The ED model auto-regressively captures full dependency among output tokens but is slow due to sequential decoding. The PIE model does parallel decoding, giving up the advantage of modeling full dependency in the output, yet it achieves accuracy competitive with the ED model for four reasons: 1. predicting edits instead of tokens, 2. labeling sequences instead of generating sequences, 3. iteratively refining predictions to capture dependencies, and 4. factorizing logits over edits and their token argument to harness pre-trained language models like BERT. Experiments on tasks spanning GEC, OCR correction and spell correction demonstrate that the PIE model is an accurate and significantly faster alternative for local sequence transduction.

local sequence transduction
gec
alternative
parallel iterative edit
text-editing method


Template-Based Information Extraction without the Templates

Abstract: Standard algorithms for template-based information extraction (IE) require predefined template schemas, and often labeled data, to learn to extract their slot fillers (e.g., an embassy is the Target of a Bombing template). This paper describes an approach to template-based IE that removes this requirement and performs extraction without knowing the template structure in advance. Our algorithm instead learns the template structure automatically from raw text, inducing template schemas as sets of linked events (e.g., bombings include detonate, set off, and destroy events) associated with semantic roles. We also solve the standard IE task, using the induced syntactic patterns to extract role fillers from specific documents. We evaluate on the MUC-4 terrorism dataset and show that we induce template structure very similar to hand-created gold structure, and we extract role fillers with an F1 score of .40, approaching the performance of algorithms that require full knowledge of the templates.

information extraction
semantic role
event template
frame


Text Understanding with the Attention Sum Reader Network

Abstract: Several large cloze-style context-question-answer datasets have been introduced recently: the CNN and Daily Mail news data and the Children's Book Test. Thanks to the size of these datasets, the associated text comprehension task is well suited for deep-learning techniques that currently seem to outperform all alternative approaches. We present a new, simple model that uses attention to directly pick the answer from the context as opposed to computing the answer using a blended representation of words in the document as is usual in similar models. This makes the model particularly suitable for question-answering problems where the answer is a single word from the document. Ensemble of our models sets new state of the art on all evaluated datasets.

reader
attention-sum
human-level performance


Learning to Predict Code-Switching Points

Abstract: Predicting possible code-switching points can help develop more accurate methods for automatically processing mixed-language text, such as multilingual language models for speech recognition systems and syntactic analyzers. We present in this paper exploratory results on learning to predict potential code-switching points in Spanish-English. We trained different learning algorithms using a transcription of code-switched discourse. To evaluate the performance of the classifiers, we used two different criteria: 1) measuring precision, recall, and F-measure of the predictions against the reference in the transcription, and 2) rating the naturalness of artificially generated code-switched sentences. Average scores for the code-switched sentences generated by our machine learning approach were close to the scores of those generated by humans.

point
learning algorithm
discourse
code alternation point
past


Fully Unsupervised Discovery of Concept-Specific Relationships by Web Mining

Abstract: We present a web mining method for discovering and enhancing relationships in which a specified concept (word class) participates. We discover a whole range of relationships focused on the given concept, rather than generic known relationships as in most previous work. Our method is based on clustering patterns that contain concept words and other words related to them. We evaluate the method on three different rich concepts and find that in each case the method generates a broad variety of relationships with good precision.

unsupervised discovery
concept word
seed
concept acquisition method


Galileo at SemEval-2020 Task 12: Multi-lingual Learning for Offensive Language Identification Using Pre-trained Language Models

Abstract: This paper describes Galileo's performance in SemEval-2020 Task 12 on detecting and categorizing offensive language in social media. For Offensive Language Identification, we proposed a multi-lingual method using Pre-trained Language Models, ERNIE and XLM-R. For offensive language categorization, we proposed a knowledge distillation method trained on soft labels generated by several supervised models. Our team participated in all three sub-tasks. In Sub-task A - Offensive Language Identification, we ranked first in terms of average F1 scores in all languages. We are also the only team which ranked among the top three across all languages. We also took the first place in Sub-task B - Automatic Categorization of Offense Types and Sub-task C - Offence Target Identification.

semeval-2020 task
offensive language identification
pre-trained language models


Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

Abstract: Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost—The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-the-art approaches focus on selecting one-best sentence or calculating soft attention weights over the set of the sentences of one specific entity pair. However, these methods are suboptimal, and the false positive problem is still a key stumbling bottleneck for the performance. We argue that those incorrectly-labeled candidate sentences must be treated with a hard decision, rather than being dealt with soft attention weights. To do this, our paper describes a radical solution—We explore a deep reinforcement learning strategy to generate the false-positive indicator, where we automatically recognize false positives for each relation type without any supervised information. Unlike the removal operation in the previous studies, we redistribute them into the negative examples. The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.

distant supervision
relation extraction
deep reinforcement learning


Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society

Abstract: With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic has been declared one of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. Addressing the issue requires solving a number of challenging problems such as identifying messages containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few. To address this gap, we release a large dataset of 16K manually annotated tweets for fine-grained disinformation analysis that (i) focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society, and (iii) covers Arabic, Bulgarian, Dutch, and English. Finally, we show strong evaluation results using pretrained Transformers, thus confirming the practical utility of the dataset in monolingual vs. multilingual, and single task vs. multitask settings.

covid-19 infodemic
journalist
fact-checker


Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking

Abstract: The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or predefined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experimental conditions. Results of an evaluation by human judges indicate that it produces not only high quality but linguistically varied utterances which are preferred compared to n-gram and rule-based systems.

dialogue system
stochastic language generation
backward rnn reranker


Conditional Causal Relationships between Emotions and Causes in Texts

Abstract: The causal relationships between emotions and causes in text have recently received a lot of attention. Most of the existing works focus on the extraction of the causally related clauses from documents. However, none of these works has considered the possibility that the causal relationships among the extracted emotion and cause clauses may only be valid under a specific context, without which the extracted clauses may not be causally related. To address such an issue, we propose a new task of determining whether or not an input pair of emotion and cause has a valid causal relationship under different contexts, and construct a corresponding dataset via manual annotation and negative sampling based on an existing benchmark dataset. Furthermore, we propose a prediction aggregation module with low computational overhead to fine-tune the prediction results based on the characteristics of the input clauses. Experiments demonstrate the effectiveness and generality of our aggregation module.


Hurdles to Progress in Long-form Question Answering

Abstract: The task of long-form question answering (LFQA) involves retrieving documents relevant to a given question and using them to generate a paragraph-length answer. While many models have recently been proposed for LFQA, we show in this paper that the task formulation raises fundamental challenges regarding evaluation and dataset creation that currently preclude meaningful modeling progress. To demonstrate these challenges, we first design a new system that relies on sparse attention and contrastive retriever learning to achieve state-of-the-art performance on the ELI5 LFQA dataset. While our system tops the public leaderboard, a detailed analysis reveals several troubling trends: (1) our system's generated answers are not actually grounded in the documents that it retrieves; (2) ELI5 contains significant train / validation overlap, as at least 81% of ELI5 validation questions occur in paraphrased form in the training set; (3) ROUGE-L is not an informative metric of generated answer quality and can be easily gamed; and (4) human evaluations used for other text generation tasks are unreliable for LFQA. We offer suggestions to mitigate each of these issues, which we hope will lead to more rigorous LFQA research and meaningful progress in the future.

long-form question
lfqa
retriever
human evaluation


Corpora for the Conceptualisation and Zoning of Scientific Papers

Abstract: We present two complementary annotation schemes for sentence based annotation of full scientific papers, CoreSC and AZ-II, applied to primary research articles in chemistry. AZ-II is the extension of AZ for chemistry papers. AZ has been shown to have been reliably annotated by independent human coders and useful for various information access tasks. Like AZ, AZ-II follows the rhetorical structure of a scientific paper and the knowledge claims made by the authors. The CoreSC scheme takes a different view of scientific papers, treating them as the humanly readable representations of scientific investigations. It seeks to retrieve the structure of the investigation from the paper as generic high-level Core Scientific Concepts (CoreSC). CoreSCs have been annotated by 16 chemistry experts over a total of 265 full papers in physical chemistry and biochemistry. We describe the differences and similarities between the two schemes in detail and present the two corpora produced using each scheme. There are 36 shared papers in the corpora, which allows us to quantitatively compare aspects of the annotation schemes. We show the correlation between the two schemes, their strengths and weeknesses and discuss the benefits of combining a rhetorical based analysis of the papers with a content-based one.

annotation scheme
research article
rhetorical structure


How Does Context Matter? On the Robustness of Event Detection with Context-Selective Mask Generalization

Abstract: Event detection (ED) aims to identify and classify event triggers in texts, which is a crucial subtask of event extraction (EE). Despite many advances in ED, the existing studies are typically centered on improving the overall performance of an ED model, which rarely consider the robustness of an ED model. This paper aims to fill this research gap by stressing the importance of robustness modeling in ED models. We first pinpoint three stark cases demonstrating the brittleness of the existing ED models. After analyzing the underlying reason, we propose a new training mechanism, called context-selective mask generalization for ED, which can effectively mine context-specific patterns for learning and robustify an ED model. The experimental results have confirmed the effectiveness of our model regarding defending against adversarial attacks, exploring unseen predicates, and tackling ambiguity cases. Moreover, a deeper analysis suggests that our approach can learn a complementary predictive bias with most ED models that use full context for feature learning.

robustness
event detection
context-selective mask generalization


How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs

Abstract: Analysing translation quality in regards to specific linguistic phenomena has historically been difficult and time-consuming. Neural machine translation has the attractive property that it can produce scores for arbitrary translations, and we propose a novel method to assess how well NMT systems model specific linguistic phenomena such as agreement over long distances, the production of novel words, and the faithful translation of polarity. The core idea is that we measure whether a reference translation is more probable under a NMT model than a contrastive translation which introduces a specific type of error. We present LingEval97, a large-scale data set of 97000 contrastive translation pairs based on the WMT English-German translation task, with errors automatically created with simple rules. We report results for a number of systems, and find that recently introduced character-level NMT systems perform better at transliteration than models with byte-pair encoding (BPE) segmentation, but perform more poorly at morphosyntactic agreement, and translating discontiguous units of meaning.

contrastive translation pair
polarity
incorrect translation


Unsupervised Personal Name Disambiguation

Abstract: This paper presents a set of algorithms for distinguishing personal names with multiple real referents in text, based on little or no supervision. The approach utilizes an unsupervised clustering technique over a rich feature space of biographic facts, which are automatically extracted via a language-independent bootstrapping process. The induced clustering of named entities are then partitioned and linked to their real referents via the automatically extracted biographic data. Performance is evaluated based on both a test set of handlabeled multi-referent personal names and via automatically generated pseudonames.

name
disambiguation
agglomerative clustering algorithm
local biographical information


DAEBAK!: Peripheral Diversity for Multilingual Word Sense Disambiguation

Abstract: We introduce Peripheral Diversity (PD) as a knowledge-based approach to achieve multilingual Word Sense Disambiguation (WSD). PD exploits the frequency and diverse use of word senses in semantic subgraphs derived from larger sense inventories such as BabelNet, Wikipedia, and WordNet in order to achieve WSD. PD’s f -measure scores for SemEval 2013 Task 12 outperform the Most Frequent Sense (MFS) baseline for two of the five languages: English, French, German, Italian, and Spanish. Despite PD remaining under-developed and under-explored, it demonstrates that it is robust, competitive, and encourages development.

peripheral diversity
word sense disambiguation
daebak


Evaluating Pronominal Anaphora in Machine Translation: An Evaluation Measure and a Test Suite

Abstract: The ongoing neural revolution in machine translation has made it easier to model larger contexts beyond the sentence-level, which can potentially help resolve some discourse-level ambiguities such as pronominal anaphora, thus enabling better translations. Unfortunately, even when the resulting improvements are seen as substantial by humans, they remain virtually unnoticed by traditional automatic evaluation measures like BLEU, as only a few words end up being affected. Thus, specialized evaluation measures are needed. With this aim in mind, we contribute an extensive, targeted dataset that can be used as a test suite for pronoun translation, covering multiple source languages and different pronoun errors drawn from real system translations, for English. We further propose an evaluation measure to differentiate good and bad pronoun translations. We also conduct a user study to report correlations with human judgments.

pronominal anaphora
machine translation
evaluation measure


Misleading Failures of Partial-input Baselines

Abstract: Recent work establishes dataset difficulty and removes annotation artifacts via partial-input baselines (e.g., hypothesis-only model for SNLI or question-only model for VQA). A successful partial-input baseline indicates that the dataset is cheatable. But the converse is not necessarily true: failures of partial-input baselines do not mean the dataset is free of artifacts. We first design artificial datasets to illustrate how the trivial patterns that are only visible in the full input can evade any partial-input baseline. Next, we identify such artifacts in the SNLI dataset—a hypothesis-only model augmented with trivial patterns in the premise can solve 15% of previously-thought “hard” examples. Our work provides a caveat for the use and creation of partial-input baselines for datasets.

partial-input baseline
artifact
premise
future dataset creation


PASSAGE: from French Parser Evaluation to Large Sized Treebank

Abstract: In this paper we present the PASSAGE project which aims at building automatically a French Treebank of large size by combining the output of several parsers, using the EASY annotation scheme. We present also the results of the of the first evaluation campaign of the project and the preliminary results we have obtained with our ROVER procedure for combining parsers automatically.

passage
parse combination algorithm
new option


GPLSI: Supervised Sentiment Analysis in Twitter using Skipgrams

Abstract: In this paper we describe the system submitted for the SemEval 2014 Task 9 (Sentiment Analysis in Twitter) Subtask B. Our contribution consists of a supervised approach using machine learning techniques, which uses the terms in the dataset as features. In this work we do not employ any external knowledge and resources. The novelty of our approach lies in the use of words, ngrams and skipgrams (notadjacent ngrams) as features, and how they are weighted.


Multi-Task Video Captioning with Video and Entailment Generation

Abstract: Video captioning, the task of describing the content of a video, has seen some promising improvements in recent years with sequence-to-sequence models, but accurately learning the temporal and logical dynamics involved in the task still remains a challenge, especially given the lack of sufficient annotated data. We improve video captioning by sharing knowledge with two related directed-generation tasks: a temporally-directed unsupervised video prediction task to learn richer context-aware video encoder representations, and a logically-directed language entailment generation task to learn better video-entailing caption decoder representations. For this, we present a many-to-many multi-task learning model that shares parameters across the encoders and decoders of the three tasks. We achieve significant improvements and the new state-of-the-art on several standard video captioning datasets using diverse automatic and human evaluations. We also show mutual multi-task improvements on the entailment generation task.

video
entailment generation
multi-task learning


Multi-Engine Machine Translation by Recursive Sentence Decomposition

Abstract: In this paper, we present a novel approach to combine the outputs of multiple MT engines into a consensus translation. In contrast to previous Multi-Engine Machine Translation (MEMT) techniques, we do not rely on word alignments of output hypotheses, but prepare the input sentence for multi-engine processing. We do this by using a recursive decomposition algorithm that produces simple chunks as input to the MT engines. A consensus translation is produced by combining the best chunk translations, selected through majority voting, a trigram language model score and a confidence score assigned to each MT engine. We report statistically significant relative improvements of up to 9% BLEU score in experiments (English→Spanish) carried out on an 800-sentence test set extracted from the Penn-II Treebank.

recursive decomposition algorithm
chunk
multi-engine machine translation


Microblogs as Parallel Corpora

Abstract: In the ever-expanding sea of microblog data, there is a surprising amount of naturally occurring parallel text: some users create post multilingual messages targeting international audiences while others “retweet” translations. We present an efficient method for detecting these messages and extracting parallel segments from them. We have been able to extract over 1M Chinese-English parallel segments from Sina Weibo (the Chinese counterpart of Twitter) using only their public APIs. As a supplement to existing parallel training data, our automatically extracted parallel data yields substantial translation quality improvements in translating microblog text and modest improvements in translating edited news commentary. The resources in described in this paper are available at http://www.cs.cmu.edu/ lingwang/utopia.

parallel data
microblog
social medium
machine translation


Using Discourse Information for Education with a Spanish-Chinese Parallel Corpus

Abstract: Nowadays, with the fruitful achievements in Natural Language Processing (NLP) studies, the concern of using NLP technologies for education has called much attention. As two of the most spoken languages in the world, Spanish and Chinese occupy important positions in both NLP studies and bilingual education. In this paper, we present a Spanish-Chinese parallel corpus with annotated discourse information that aims to serve for bilingual language education. The theoretical framework of this work is Rhetorical Structure Theory (RST). The corpus is composed of 100 Spanish-Chinese parallel texts, and all the discourse markers (DM) have been annotated to form the education source. With pedagogical aim, we also present two programs that generate automatic exercises for both Spanish and Chinese students using our corpus. The reliability of this work has been evaluated using Kappa coefficient.

discourse information
education
spanish-chinese parallel corpus


Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection

Abstract: Despite significant progress in neural abstractive summarization, recent studies have shown that the current models are prone to generating summaries that are unfaithful to the original context. To address the issue, we study contrast candidate generation and selection as a model-agnostic post-processing technique to correct the extrinsic hallucinations (i.e. information not present in the source text) in unfaithful summaries. We learn a discriminative correction model by generating alternative candidate summaries where named entities and quantities in the generated summary are replaced with ones with compatible semantic types from the source document. This model is then used to select the best candidate as the final output summary. Our experiments and analysis across a number of neural summarization systems show that our proposed method is effective in identifying and correcting extrinsic hallucinations. We analyze the typical hallucination phenomenon by different types of neural summarization systems, in hope to provide insights for future work on the direction.

faithfulness
abstractive summarization
selection


From Speaker Identification to Affective Analysis: A Multi-Step System for Analyzing Children's Stories

Abstract: We propose a multi-step system for the analysis of children’s stories that is intended to be part of a larger text-to-speechbased storytelling system. A hybrid approach is adopted, where pattern-based and statistical methods are used along with utilization of external knowledge sources. This system performs the following story analysis tasks: identification of characters in each story; attribution of quotes to specific story characters; identification of character age, gender and other salient personality attributes; and finally, affective analysis of the quoted material. The different types of analyses were evaluated using several datasets. For the quote attribution, as well as for the gender and age estimation, substantial improvement over baseline was realized, whereas results for personality attribute estimation and valence estimation are more modest.

affective analysis
multi-step system
story


Towards Debiasing Sentence Representations

Abstract: As natural language processing methods are increasingly deployed in real-world scenarios such as healthcare, legal systems, and social science, it becomes necessary to recognize the role they potentially play in shaping social biases and stereotypes. Previous work has revealed the presence of social biases in widely used word embeddings involving gender, race, religion, and other social constructs. While some methods were proposed to debias these word-level embeddings, there is a need to perform debiasing at the sentence-level given the recent shift towards new contextualized sentence representations such as ELMo and BERT. In this paper, we investigate the presence of social biases in sentence-level representations and propose a new method, Sent-Debias, to reduce these biases. We show that Sent-Debias is effective in removing biases, and at the same time, preserves performance on sentence-level downstream tasks such as sentiment analysis, linguistic acceptability, and natural language understanding. We hope that our work will inspire future research on characterizing and removing social biases from widely adopted sentence representations for fairer NLP.

sentence representation
gender
bert
sent-debias


BACO: A Background Knowledge- and Content-Based Framework for Citing Sentence Generation

Abstract: In this paper, we focus on the problem of citing sentence generation, which entails generating a short text to capture the salient information in a cited paper and the connection between the citing and cited paper. We present BACO, a BAckground knowledge- and COntent-based framework for citing sentence generation, which considers two types of information: (1) background knowledge by leveraging structural information from a citation network; and (2) content, which represents in-depth information about what to cite and why to cite. First, a citation network is encoded to provide background knowledge. Second, we apply salience estimation to identify what to cite by estimating the importance of sentences in the cited paper. During the decoding stage, both types of information are combined to facilitate the text generation, and then we conduct a joint training for the generator and citation function classification to make the model aware of why to cite. Our experimental results show that our framework outperforms comparative baselines.

background knowledge-
sentence generation
cited paper


CIS-positive: A Combination of Convolutional Neural Networks and Support Vector Machines for Sentiment Analysis in Twitter

Abstract: This paper describes our automatic sentiment analysis system – CIS-positive – for SemEval 2015 Task 10 “Sentiment Analysis in Twitter”, subtask B “Message Polarity Classification”. In this system, we propose to normalize the Twitter data in a way that maximizes the coverage of sentiment lexicons and minimizes distracting elements. Furthermore, we integrate the output of Convolutional Neural Networks into Support Vector Machines for the polarity classification. Our system achieves a macro F1 score of the positive and negative class of 59.57 on the SemEval 2015 test data.

convolutional neural networks
sentiment analysis
twitter


EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks

Abstract: Accurate detection of emotion from natural language has applications ranging from building emotional chatbots to better understanding individuals and their lives. However, progress on emotion detection has been hampered by the absence of large labeled datasets. In this work, we build a very large dataset for fine-grained emotions and develop deep learning models on it. We achieve a new state-of-the-art on 24 fine-grained types of emotions (with an average accuracy of 87.58%). We also extend the task beyond emotion types to model Robert Plutick's 8 primary emotion dimensions, acquiring a superior accuracy of 95.68%.

emotion
deep learning
distant supervision
hashtag
writer


Modeling Biological Processes for Reading Comprehension

Abstract: Machine reading calls for programs that read and understand text, but most current work only attempts to extract facts from redundant web-scale corpora. In this paper, we focus on a new reading comprehension task that requires complex reasoning over a single document. The input is a paragraph describing a biological process, and the goal is to answer questions that require an understanding of the relations between entities and events in the process. To answer the questions, we first predict a rich structure representing the process in the paragraph. Then, we map the question to a formal query, which is executed against the predicted structure. We demonstrate that answering questions via predicted structures substantially improves accuracy over baselines that use shallower representations.

biological process
reading comprehension task
reasoning
event argument
processbank dataset


GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

Abstract: In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.

discourse unit segmentation
connective detection
gumdrop


Rumor Detection on Twitter Using Multiloss Hierarchical BiLSTM with an Attenuation Factor

Abstract: Social media platforms such as Twitter have become a breeding ground for unverified information or rumors. These rumors can threaten people's health, endanger the economy, and affect the stability of a country. Many researchers have developed models to classify rumors using traditional machine learning or vanilla deep learning models. However, previous studies on rumor detection have achieved low precision and are time consuming. Inspired by the hierarchical model and multitask learning, a multiloss hierarchical BiLSTM model with an attenuation factor is proposed in this paper. The model is divided into two BiLSTM modules: post level and event level. By means of this hierarchical structure, the model can extract deep information from limited quantities of text. Each module has a loss function that helps to learn bilateral features and reduce the training time. An attenuation factor is added at the post level to increase the accuracy. The results on two rumor datasets demonstrate that our model achieves better performance than that of state-of-the-art machine learning and vanilla deep learning models.


Graph-Based Generation of Referring Expressions

Abstract: This article describes a new approach to the generation of referring expressions. We propose to formalize a scene (consisting of a set of objects with various properties and relations) as a labeled directed graph and describe content selection (which properties to include in a referring expression) as a subgraph construction problem. Cost functions are used to guide the search process and to give preference to some solutions over others. The current approach has four main advantages: (1) Graph structures have been studied extensively, and by moving to a graph perspective we get direct access to the many theories and algorithms for dealing with graphs; (2) many existing generation algorithms can be reformulated in terms of graphs, and this enhances comparison and integration of the various approaches; (3) the graph perspective allows us to solve a number of problems that have plagued earlier algorithms for the generation of referring expressions; and (4) the combined use of graphs and cost functions paves the way for an integration of rule-based generation techniques with more recent stochastic approaches.

referring expression
natural language generation
graph-based approach


Dynamic Generation of Cooperative Natural Language Responses in WEBCOOP

Abstract: We present in this paper a formal approach for the dynamic generation of cooperative NL responses in WEBCOOP, a system that provides intelligent responses in French to natural language queries on the Web. The system integrates reasoning procedures and NLG techniques paired with hypertext links. Content determination is organized in two steps: providing explanations that report user misconceptions and then offering flexible solutions that reflect the cooperative know howof the system.

natural language response
webcoop
dynamic generation
cooperative question


Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning

Abstract: Visual language grounding is widely studied in modern neural image captioning systems, which typically adopts an encoder-decoder framework consisting of two principal components: a convolutional neural network (CNN) for image feature extraction and a recurrent neural network (RNN) for language caption generation. To study the robustness of language grounding to adversarial perturbations in machine vision and perception, we propose Show-and-Fool, a novel algorithm for crafting adversarial examples in neural image captioning. The proposed algorithm provides two evaluation approaches, which check if we can mislead neural image captioning systems to output some randomly chosen captions or keywords. Our extensive experiments show that our algorithm can successfully craft visually-similar adversarial examples with randomly targeted captions or keywords, and the adversarial examples can be made highly transferable to other image captioning systems. Consequently, our approach leads to new robustness implications of neural image captioning and novel insights in visual language grounding.

visual language grounding
adversarial example
neural image captioning


Conundrums in Unsupervised Keyphrase Extraction: Making Sense of the State-of-the-Art

Abstract: State-of-the-art approaches for unsupervised keyphrase extraction are typically evaluated on a single dataset with a single parameter setting. Consequently, it is unclear how effective these approaches are on a new dataset from a different domain, and how sensitive they are to changes in parameter settings. To gain a better understanding of state-of-the-art unsupervised keyphrase extraction algorithms, we conduct a systematic evaluation and analysis of these algorithms on a variety of standard evaluation datasets.

unsupervised keyphrase extraction
tf-idf
ranking


CaTeRS: Causal and Temporal Relation Scheme for Semantic Annotation of Event Structures

Abstract: Learning commonsense causal and temporal relation between events is one of the major steps towards deeper language understanding. This is even more crucial for understanding stories and script learning. A prerequisite for learning scripts is a semantic framework which enables capturing rich event structures. In this paper we introduce a novel semantic annotation framework, called Causal and Temporal Relation Scheme (CaTeRS), which is unique in simultaneously capturing a comprehensive set of temporal and causal relations between events. By annotating a total of 1,600 sentences in the context of 320 five-sentence short stories sampled from ROCStories corpus, we demonstrate that these stories are indeed full of causal and temporal relations. Furthermore, we show that the CaTeRS annotation scheme enables high inter-annotator agreement for broad-coverage event entity annotation and moderate agreement on semantic link annotation.

causal
temporal relation scheme
caters annotation scheme
commonsense reasoning standpoint


Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection

Abstract: Most existing approaches to disfluency detection heavily rely on human-annotated corpora, which is expensive to obtain in practice. There have been several proposals to alleviate this issue with, for instance, self-supervised learning techniques, but they still require human-annotated corpora. In this work, we explore the unsupervised learning paradigm which can potentially work with unlabeled text corpora that are cheaper and easier to obtain. Our model builds upon the recent work on Noisy Student Training, a semi-supervised learning approach that extends the idea of self-training. Experimental results on the commonly used English Switchboard test set show that our approach achieves competitive performance compared to the previous state-of-the-art supervised systems using contextualized word embeddings (e.g. BERT and ELECTRA).

self-training
disfluency detection
pseudo label
teacher


Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings

Abstract: We present a natural language generator based on the sequence-to-sequence approach that can be trained to produce natural language strings as well as deep syntax dependency trees from input dialogue acts, and we use it to directly compare two-step generation with separate sentence planning and surface realization stages to a joint, one-step approach. We were able to train both setups successfully using very little training data. The joint setup offers better performance, surpassing state-of-the-art with regards to n-gram-based scores while providing more relevant outputs.

language generator
sentence planning
encoder-decoder model


EMA at SemEval-2018 Task 1: Emotion Mining for Arabic

Abstract: While significant progress has been achieved for Opinion Mining in Arabic (OMA), very limited efforts have been put towards the task of Emotion mining in Arabic. In fact, businesses are interested in learning a fine-grained representation of how users are feeling towards their products or services. In this work, we describe the methods used by the team Emotion Mining in Arabic (EMA), as part of the SemEval-2018 Task 1 for Affect Mining for Arabic tweets. EMA participated in all 5 subtasks. For the five tasks, several preprocessing steps were evaluated and eventually the best system included diacritics removal, elongation adjustment, replacement of emojis by the corresponding Arabic word, character normalization and light stemming. Moreover, several features were evaluated along with different classification and regression techniques. For the 5 subtasks, word embeddings feature turned out to perform best along with Ensemble technique. EMA achieved the 1st place in subtask 5, and 3rd place in subtasks 1 and 3.


TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising

Abstract: Text summarization aims to extract essential information from a piece of text and transform the text into a concise version. Existing unsupervised abstractive summarization models leverage recurrent neural networks framework while the recently proposed transformer exhibits much more capability. Moreover, most of previous summarization models ignore abundant unlabeled corpora resources available for pretraining. In order to address these issues, we propose TED, a transformer-based unsupervised abstractive summarization system with pretraining on large-scale data. We first leverage the lead bias in news articles to pretrain the model on millions of unlabeled corpora. Next, we finetune TED on target domains through theme modeling and a denoising autoencoder to enhance the quality of generated summaries. Notably, TED outperforms all unsupervised abstractive baselines on NYT, CNN/DM and English Gigaword datasets with various document styles. Further analysis shows that the summaries generated by TED are highly abstractive, and each component in the objective function of TED is highly effective.


The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives

Abstract: We seek to understand how the representations of individual tokens and the structure of the learned feature space evolve between layers in deep neural networks under different learning objectives. We chose the Transformers for our analysis as they have been shown effective with various tasks, including machine translation (MT), standard left-to-right language models (LM) and masked language modeling (MLM). Previous work used black-box probing tasks to show that the representations learned by the Transformer differ significantly depending on the objective. In this work, we use canonical correlation analysis and mutual information estimators to study how information flows across Transformer layers and observe that the choice of the objective determines this process. For example, as you go from bottom to top layers, information about the past in left-to-right language models gets vanished and predictions about the future get formed. In contrast, for MLM, representations initially acquire information about the context around the token, partially forgetting the token identity and producing a more generalized token representation. The token identity then gets recreated at the top MLM layers.

evolution
machine translation
information flow
transformer layer
gpt-2


Pooled Contextualized Embeddings for Named Entity Recognition

Abstract: Contextual string embeddings are a recent type of contextualized word embedding that were shown to yield state-of-the-art results when utilized in a range of sequence labeling tasks. They are based on character-level language models which treat text as distributions over characters and are capable of generating embeddings for any string of characters within any textual context. However, such purely character-based approaches struggle to produce meaningful embeddings if a rare string is used in a underspecified context. To address this drawback, we propose a method in which we dynamically aggregate contextualized embeddings of each unique string that we encounter. We then use a pooling operation to distill a ”global” word representation from all contextualized instances. We evaluate these ”pooled contextualized embeddings” on common named entity recognition (NER) tasks such as CoNLL-03 and WNUT and show that our approach significantly improves the state-of-the-art for NER. We make all code and pre-trained models available to the research community for use and reproduction.

entity recognition
language model
underspecified context
pre-trained model


Automatically Generating Rhythmic Verse with Neural Networks

Abstract: We propose two novel methodologies for the automatic generation of rhythmic poetry in a variety of forms. The first approach uses a neural language model trained on a phonetic encoding to learn an implicit representation of both the form and content of English poetry. This model can effectively learn common poetic devices such as rhyme, rhythm and alliteration. The second approach considers poetry generation as a constraint satisfaction problem where a generative neural language model is tasked with learning a representation of content, and a discriminative weighted finite state machine constrains it on the basis of form. By manipulating the constraints of the latter model, we can generate coherent poetry with arbitrary forms and themes. A large-scale extrinsic evaluation demonstrated that participants consider machine-generated poems to be written by humans 54% of the time. In addition, participants rated a machine-generated poem to be the best amongst all evaluated.


Weighting Finite-State Transductions With Neural Context

Abstract: How should one apply deep learning to tasks such as morphological reinﬂection, which stochastically edit one string to get another? A recent approach to such sequence-to-sequence tasks is to compress the input string into a vector that is then used to generate the output string, using recurrent neural networks. In contrast, we propose to keep the traditional architecture, which uses a ﬁnite-state transducer to score all possible output strings , but to augment the scoring function with the help of recurrent networks. A stack of bidirectional LSTMs reads the input string from left-to-right and right-to-left, in order to summarize the input context in which a transducer arc is applied. We combine these learned features with the transducer to deﬁne a probability distribution over aligned output strings, in the form of a weighted ﬁnite-state automaton. This reduces hand-engineering of features, allows learned features to examine unbounded context in the input string, and still permits exact inference through dynamic programming. We illustrate our method on the tasks of morphological reinﬂection and lemmatization.

morphology
transducer
possible output string
bidirectional lstm
finite-state transducer


Open Resources and Tools for the Shallow Processing of Portuguese: The TagShare Project

Abstract: This paper presents the TagShare project and the linguistic resources and tools for the shallow processing of Portuguese developed in its scope. These resources include a 1 million token corpus that has been accurately hand annotated with a variety of linguistic information, as well as several state of the art shallow processing tools capable of automatically producing that type of annotation. At present, the linguistic annotations in the corpus are sentence and paragraph boundaries, token boundaries, morphosyntactic POS categories, values of inflection features, lemmas and namedentities. Hence, the set of tools comprise a sentence chunker, a tokenizer, a POS tagger, nominal and verbal analyzers and lemmatizers, a verbal conjugator, a nominal inflector, and a namedentity recognizer, some of which underline several online services.

shallow processing
portuguese
tagshare project


Automatically Assessing Machine Summary Content Without a Gold Standard

Abstract: The most widely adopted approaches for evaluation of summary content follow some protocol for comparing a summary with gold-standard human summaries, which are traditionally called model summaries. This evaluation paradigm falls short when human summaries are not available and becomes less accurate when only a single model is available. We propose three novel evaluation techniques. Two of them are model-free and do not rely on a gold standard for the assessment. The third technique improves standard automatic evaluations by expanding the set of available model summaries with chosen system summaries.We show that quantifying the similarity between the source text and its summary with appropriately chosen measures produces summary scores which replicate human assessments accurately. We also explore ways of increasing evaluation quality when only one human model summary is available as a gold standard. We introduce pseudomodels, which are system summaries deemed to contain good content according to automatic evaluation. Combining the pseudomodels with the single human model to form the gold-standard leads to higher correlations with human judgments compared to using only the one available model. Finally, we explore the feasibility of another measure—similarity between a system summary and the pool of all other system summaries for the same input. This method of comparison with the consensus of systems produces impressively accurate rankings of system summaries, achieving correlation with human rankings above 0.9.


Abu-MaTran at WMT 2015 Translation Task: Morphological Segmentation and Web Crawling

Abstract: This paper presents the machine translation systems submitted by the Abu-MaTran project for the Finnish‐English language pair at the WMT 2015 translation task. We tackle the lack of resources and complex morphology of the Finnish language by (i) crawling parallel and monolingual data from the Web and (ii) applying rule-based and unsupervised methods for morphological segmentation. Several statistical machine translation approaches are evaluated and then combined to obtain our final submissions, which are the top performing English-to-Finnish unconstrained (all automatic metrics) and constrained (BLEU), and Finnish-to-English constrained (TER) systems.

wmt
translation task
morphological segmentation


Neural Metaphor Detecting with CNN-LSTM Model

Abstract: Metaphors are figurative languages widely used in daily life and literatures. It's an important task to detect the metaphors evoked by texts. Thus, the metaphor shared task is aimed to extract metaphors from plain texts at word level. We propose to use a CNN-LSTM model for this task. Our model combines CNN and LSTM layers to utilize both local and long-range contextual information for identifying metaphorical information. In addition, we compare the performance of the softmax classifier and conditional random field (CRF) for sequential labeling in this task. We also incorporated some additional features such as part of speech (POS) tags and word cluster to improve the performance of model. Our best model achieved 65.06% F-score in the all POS testing subtask and 67.15% in the verbs testing subtask.


Towards Robust Neural Machine Translation

Abstract: Small perturbations in the input can severely distort intermediate representations and thus impact translation quality of neural machine translation (NMT) models. In this paper, we propose to improve the robustness of NMT models with adversarial stability training. The basic idea is to make both the encoder and decoder in NMT models robust against input perturbations by enabling them to behave similarly for the original input and its perturbed counterpart. Experimental results on Chinese-English, English-German and English-French translation tasks show that our approaches can not only achieve significant improvements over strong NMT systems but also improve the robustness of NMT models.

neural machine translation
adversarial stability training
noise
feature level


Gender and sentiment, critics and authors: a dataset of Norwegian book reviews

Abstract: Gender bias in models and datasets is widely studied in NLP. The focus has usually been on analysing how females and males express themselves, or how females and males are described. However, a less studied aspect is the combination of these two perspectives, how female and male describe the same or opposite gender. In this paper, we present a new gender annotated sentiment dataset of critics reviewing the works of female and male authors. We investigate if this newly annotated dataset contains differences in how the works of male and female authors are critiqued, in particular in terms of positive and negative sentiment. We also explore the differences in how this is done by male and female critics. We show that there are differences in how critics assess the works of authors of the same or opposite gender. For example, male critics rate crime novels written by females, and romantic and sentimental works written by males, more negatively.


Distortion Models for Statistical Machine Translation

Abstract: In this paper, we argue that n-gram language models are not sufficient to address word reordering required for Machine Translation. We propose a new distortion model that can be used with existing phrase-based SMT decoders to address those n-gram language model limitations. We present empirical results in Arabic to English Machine Translation that show statistically significant improvements when our proposed model is used. We also propose a novel metric to measure word order similarity (or difference) between any pair of languages based on word alignments.

distortion model
scope
deterministic choice


Template-free Prompt Tuning for Few-shot NER

Abstract: Prompt-based methods have been successfully applied in sentence-level few-shot learning tasks, mostly owing to the sophisticated design of templates and label words. However, when applied to token-level labeling tasks such as NER, it would be time-consuming to enumerate the template queries over all potential entity spans. In this work, we propose a more elegant method to reformulate NER tasks as LM problems without any templates. Specifically, we discard the template construction process while maintaining the word prediction paradigm of pre-training models to predict a class-related pivot word (or label word) at the entity position. Meanwhile, we also explore principled ways to automatically search for appropriate label words that the pre-trained models can easily adapt to. While avoiding the complicated template-based process, the proposed LM objective also reduces the gap between different objectives used in pre-training and fine-tuning, thus it can better benefit the few-shot performance. Experimental results demonstrate the effectiveness of the proposed method over bert-tagger and template-based method under few-shot settings. Moreover, the decoding speed of the proposed method is up to 1930.12 times faster than the template-based method.

prompt tuning
few-shot setting
low-resource ner


GHKM Rule Extraction and Scope-3 Parsing in Moses

Abstract: We developed a string-to-tree system for English--German, achieving competitive results against a hierarchical model baseline. We provide details of our implementation of GHKM rule extraction and scope-3 parsing in the Moses toolkit. We compare systems trained on the same data using different grammar extraction methods.


Detecting Speech Repairs Incrementally Using a Noisy Channel Approach

Abstract:
Unrehearsed spoken language often contains disfluencies. In order to correctly interpret a spoken utterance, any such disfluencies must be identified and removed or otherwise dealt with. Operating on transcripts of speech which contain disfluencies, our particular focus here is the identification and correction of speech repairs using a noisy channel model. Our aim is to develop a high-accuracy mechanism that can identify speech repairs in an incremental fashion, as the utterance is processed word-by-word.

We also address the issue of the evaluation of such incremental systems. We propose a novel approach to evaluation, which evaluates performance in detecting and correcting disfluencies incrementally, rather than only assessing performance once the processing of an utterance is complete. This demonstrates some shortcomings in our basic incremental model, and so we then demonstrate a technique that improves performance on the detection of disfluencies as they happen.


Chinese Syntactic Reordering for Statistical Machine Translation

Abstract: Syntactic reordering approaches are an effective method for handling word-order differences between source and target languages in statistical machine translation (SMT) systems. This paper introduces a reordering approach for translation from Chinese to English. We describe a set of syntactic reordering rules that exploit systematic differences between Chinese and English word order. The resulting system is used as a preprocessor for both training and test sentences, transforming Chinese sentences to be much closer to English in terms of their word order. We evaluated the reordering approach within the MOSES phrase-based SMT system (Koehn et al., 2007). The reordering approach improved the BLEU score for the MOSES system from 28.52 to 30.86 on the NIST 2006 evaluation data. We also conducted a series of experiments to analyze the accuracy and impact of different types of reordering rules.

statistical machine translation
clause restructuring
structural difference
preprocessing step
localizer phrase


Statistical Parsing of Morphologically Rich Languages (SPMRL) What, How and Whither

Abstract: The term Morphologically Rich Languages (MRLs) refers to languages in which significant information concerning syntactic units and relations is expressed at word-level. There is ample evidence that the application of readily available statistical parsing models to such languages is susceptible to serious performance degradation. The first workshop on statistical parsing of MRLs hosts a variety of contributions which show that despite language-specific idiosyncrasies, the problems associated with parsing MRLs cut across languages and parsing frameworks. In this paper we review the current state-of-affairs with respect to parsing MRLs and point out central challenges. We synthesize the contributions of researchers working on parsing Arabic, Basque, French, German, Hebrew, Hindi and Korean to point out shared solutions across languages. The overarching analysis suggests itself as a source of directions for future investigations.

morphologically rich languages
parsing model
characteristic
constituency parser


NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large Datasets

Abstract: Existing tools for Question Answering (QA) have challenges that limit their use in practice. They can be complex to set up or integrate with existing infrastructure, do not offer configurable interactive interfaces, and do not cover the full set of subtasks that frequently comprise the QA pipeline (query expansion, retrieval, reading, and explanation/sensemaking). To help address these issues, we introduce NeuralQA - a usable library for QA on large datasets. NeuralQA integrates well with existing infrastructure (e.g., ElasticSearch instances and reader models trained with the HuggingFace Transformers API) and offers helpful defaults for QA subtasks. It introduces and implements contextual query expansion (CQE) using a masked language model (MLM) as well as relevant snippets (RelSnip) - a method for condensing large documents into smaller passages that can be speedily processed by a document reader model. Finally, it offers a flexible user interface to support workflows for research explorations (e.g., visualization of gradient-based explanations to support qualitative inspection of model behaviour) and large scale search deployment. Code and documentation for NeuralQA is available as open source on Github.

question answering
large dataset
neuralqa


Decomposing and Comparing Meaning Relations: Paraphrasing, Textual Entailment, Contradiction, and Specificity

Abstract: In this paper, we present a methodology for decomposing and comparing multiple meaning relations (paraphrasing, textual entailment, contradiction, and specificity). The methodology includes SHARel - a new typology that consists of 26 linguistic and 8 reason-based categories. We use the typology to annotate a corpus of 520 sentence pairs in English and we demonstrate that unlike previous typologies, SHARel can be applied to all relations of interest with a high inter-annotator agreement. We analyze and compare the frequency and distribution of the linguistic and reason-based phenomena involved in paraphrasing, textual entailment, contradiction, and specificity. This comparison allows for a much more in-depth analysis of the workings of the individual relations and the way they interact and compare with each other. We release all resources (typology, annotation guidelines, and annotated corpus) to the community.

meaning relation
contradiction
specificity


Weighted Alignment Matrices for Statistical Machine Translation

Abstract: Current statistical machine translation systems usually extract rules from bilingual corpora annotated with 1-best alignments. They are prone to learn noisy rules due to alignment mistakes. We propose a new structure called weighted alignment matrix to encode all possible alignments for a parallel text compactly. The key idea is to assign a probability to each word pair to indicate how well they are aligned. We design new algorithms for extracting phrase pairs from weighted alignment matrices and estimating their probabilities. Our experiments on multiple language pairs show that using weighted matrices achieves consistent improvements over using n-best lists in significant less extraction time.

alignment matrix
n-best list
parallel sentence
posterior probability


TIPSem (English and Spanish): Evaluating CRFs and Semantic Roles in TempEval-2

Abstract: This paper presents TIPSem, a system to extract temporal information from natural language texts for English and Spanish. TIPSem, learns CRF models from training data. Although the used features include different language analysis levels, the approach is focused on semantic information. For Spanish, TIPSem achieved the best F1 score in all the tasks. For English, it obtained the best F1 in tasks B (events) and D (event-dct links); and was among the best systems in the rest.


Interoperability of Dialogue Corpora through ISO 24617-2-based Querying

Abstract: This paper explores a way of achieving interoperability: developing a query format for accessing existing annotated corpora whose expressions make use of the annotation language defined by the standard. The interpretation of expressions in the query implements a mapping from ISO 24617-2 concepts to those of the annotation scheme used in the corpus. We discuss two possible ways to query existing annotated corpora using DiAML. One way is to transform corpora into DiAML compliant format, and subsequently query these data using XQuery or XPath. The second approach is to define a DiAML query that can be directly used to retrieve requested information from the annotated data. Both approaches are valid. The first one presents a standard way of querying XML data. The second approach is a DiAML-oriented querying of dialogue act annotated data, for which we designed an interface. The proposed approach is tested on two important types of existing dialogue corpora: spoken two-person dialogue corpora collected and annotated within the HCRC Map Task paradigm, and multiparty face-to-face dialogues of the AMI corpus. We present the results and evaluate them with respect to accuracy and completeness through statistical comparisons between retrieved and manually constructed reference annotations.

dialogue corpora
querying
interoperability


Data-Driven Parsing with Probabilistic Linear Context-Free Rewriting Systems

Abstract: This paper presents the first efficient implementation of a weighted deductive CYK parser for Probabilistic Linear Context-Free Rewriting Systems (PLCFRSs). LCFRS, an extension of CFG, can describe discontinuities in a straightforward way and is therefore a natural candidate to be used for data-driven parsing. To speed up parsing, we use different context-summary estimates of parse items, some of them allowing for A* parsing. We evaluate our parser with grammars extracted from the German NeGra treebank. Our experiments show that data-driven LCFRS parsing is feasible and yields output of competitive quality.


Neural Machine Translation of Logographic Language Using Sub-character Level Information

Abstract: Recent neural machine translation (NMT) systems have been greatly improved by encoder-decoder models with attention mechanisms and sub-word units. However, important differences between languages with logographic and alphabetic writing systems have long been overlooked. This study focuses on these differences and uses a simple approach to improve the performance of NMT systems utilizing decomposed sub-character level information for logographic languages. Our results indicate that our approach not only improves the translation capabilities of NMT systems between Chinese and English, but also further improves NMT systems between Chinese and Japanese, because it utilizes the shared information brought by similar sub-character units.

logographic language
sub-character level information
neural machine translation


Unbabel's Submission to the WMT2019 APE Shared Task: BERT-Based Encoder-Decoder for Automatic Post-Editing

Abstract: This paper describes Unbabel's submission to the WMT2019 APE Shared Task for the English-German language pair. Following the recent rise of large, powerful, pre-trained models, we adapt the BERT pretrained model to perform Automatic Post-Editing in an encoder-decoder framework. Analogously to dual-encoder architectures we develop a BERT-based encoder-decoder (BED) model in which a single pretrained BERT encoder receives both the source src and machine translation mt strings. Furthermore, we explore a conservativeness factor to constrain the APE system to perform fewer edits. As the official results show, when trained on a weighted combination of in-domain and artificial training data, our BED system with the conservativeness penalty improves significantly the translations of a strong NMT system by -0.78 and +1.23 in terms of TER and BLEU, respectively. Finally, our submission achieves a new state-of-the-art, ex-aequo, in English-German APE of NMT.

submission
encoder-decoder
automatic post-editing


SegBo: A Database of Borrowed Sounds in the World's Languages

Abstract: Phonological segment borrowing is a process through which languages acquire new contrastive speech sounds as the result of borrowing new words from other languages. Despite the fact that phonological segment borrowing is documented in many of the world's languages, to date there has been no large-scale quantitative study of the phenomenon. In this paper, we present SegBo, a novel cross-linguistic database of borrowed phonological segments. We describe our data aggregation pipeline and the resulting language sample. We also present two short case studies based on the database. The first deals with the impact of large colonial languages on the sound systems of the world's languages; the second deals with universals of borrowing in the domain of rhotic consonants.

database
world
phonological segment
cross-linguistic database
segbo


INFOTABS: Inference on Tables as Semi-structured Data

Abstract: In this paper, we observe that semi-structured tabulated text is ubiquitous; understanding them requires not only comprehending the meaning of text fragments, but also implicit relationships between them. We argue that such data can prove as a testing ground for understanding how we reason about information. To study this, we introduce a new dataset called INFOTABS, comprising of human-written textual hypotheses based on premises that are tables extracted from Wikipedia info-boxes. Our analysis shows that the semi-structured, multi-domain and heterogeneous nature of the premises admits complex, multi-faceted reasoning. Experiments reveal that, while human annotators agree on the relationships between a table-hypothesis pair, several standard modeling strategies are unsuccessful at the task, suggesting that reasoning about tables can pose a difficult modeling challenge.

table
infotabs
natural language inference
claim


WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse

Abstract: We release a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence. We use the collected data to show that the language generated during editing differs from the language that we observe in standard corpora, and that models trained on edits encode different aspects of semantics and discourse than models trained on raw text. We release the full corpus as a resource to aid ongoing research in semantics, discourse, and representation learning.

discourse
atomic edit
wikiatomicedit
sentence-to-sentence generation task


MICRON: Multigranular Interaction for Contextualizing RepresentatiON in Non-factoid Question Answering

Abstract: This paper studies the problem of non-factoid question answering, where the answer may span over multiple sentences. Existing solutions can be categorized into representation- and interaction-focused approaches. We combine their complementary strength, by a hybrid approach allowing multi-granular interactions, but represented at word level, enabling an easy integration with strong word-level signals. Specifically, we propose MICRON: Multigranular Interaction for Contextualizing RepresentatiON, a novel approach which derives contextualized uni-gram representation from n-grams. Our contributions are as follows: First, we enable multi-granular matches between question and answer n-grams. Second, by contextualizing word representation with surrounding n-grams, MICRON can naturally utilize word-based signals for query term weighting, known to be effective in information retrieval. We validate MICRON in two public non-factoid question answering datasets: WikiPassageQA and InsuranceQA, showing our model achieves the state of the art among baselines with reported performances on both datasets.

multigranular interaction
contextualizing representation
non-factoid question answering


How effective is BERT without word ordering? Implications for language understanding and data privacy

Abstract: Ordered word sequences contain the rich structures that define language. However, it's often not clear if or how modern pretrained language models utilize these structures. We show that the token representations and self-attention activations within BERT are surprisingly resilient to shuffling the order of input tokens, and that for several GLUE language understanding tasks, shuffling only minimally degrades performance, e.g., by 4% for QNLI. While bleak from the perspective of language understanding, our results have positive implications for cases where copyright or ethics necessitates the consideration of bag-of-words data (vs. full documents). We simulate such a scenario for three sensitive classification tasks, demonstrating minimal performance degradation vs. releasing full language sequences.


Head-Driven Phrase Structure Grammar Parsing on Penn Treebank

Abstract: Head-driven phrase structure grammar (HPSG) enjoys a uniform formalism representing rich contextual syntactic and even semantic meanings. This paper makes the first attempt to formulate a simplified HPSG by integrating constituent and dependency formal representations into head-driven phrase structure. Then two parsing algorithms are respectively proposed for two converted tree representations, division span and joint span. As HPSG encodes both constituent and dependency structure information, the proposed HPSG parsers may be regarded as a sort of joint decoder for both types of structures and thus are evaluated in terms of extracted or converted constituent and dependency parsing trees. Our parser achieves new state-of-the-art performance for both parsing tasks on Penn Treebank (PTB) and Chinese Penn Treebank, verifying the effectiveness of joint learning constituent and dependency structures. In details, we report 95.84 F1 of constituent parsing and 97.00% UAS of dependency parsing on PTB.


Detecting Disagreement in Conversations using Pseudo-Monologic Rhetorical Structure

Abstract: Casual online forums such as Reddit, Slashdot and Digg, are continuing to increase in popularity as a means of communication. Detecting disagreement in this domain is a considerable challenge. Many topics are unique to the conversation on the forum, and the appearance of disagreement may be much more subtle than on political blogs or social media sites such as twitter. In this analysis we present a crowd-sourced annotated corpus for topic level disagreement detection in Slashdot, showing that disagreement detection in this domain is difficult even for humans. We then proceed to show that a new set of features determined from the rhetorical structure of the conversation significantly improves the performance on disagreement detection over a baseline consisting of unigram/bigram features, discourse markers, structural features and meta-post features.

disagreement
conversation
rhetorical structure