A Collaborative Framework for Collecting Thai Unknown Words from the Web

Abstract: We propose a collaborative framework for collecting Thai unknown words found on Web pages over the Internet. Our main goal is to design and construct a Web-based system which allows a group of interested users to participate in constructing a Thai unknown-word open dictionary. The proposed framework provides supporting algorithms and tools for automatically identifying and extracting unknown words from Web pages of given URLs. The system yields the result of unknown-word candidates which are presented to the users for verification. The approved unknown words could be combined with the set of existing words in the lexicon to improve the performance of many NLP tasks such as word segmentation, information retrieval and machine translation. Our framework includes word segmentation and morphological analysis modules for handling the non-segmenting characteristic of Thai written language. To take advantage of large available text resource on the Web, our unknown-word boundary identification approach is based on the statistical string pattern-matching algorithm.

collaborative framework
unknown word
web


bs,hr,srWaC - Web Corpora of Bosnian, Croatian and Serbian

Abstract: In this paper we present the construction process of top-level-domain web corpora of Bosnian, Croatian and Serbian. For constructing the corpora we use the SpiderLing crawler with its associated tools adapted for simultaneous crawling and processing of text written in two scripts, Latin and Cyrillic. In addition to the modified collection process we focus on two sources of noise in the resulting corpora: 1. they contain documents written in the other, closely related languages that can not be identified with standard language identification methods and 2. as most web corpora, they partially contain low-quality data not suitable for the specific research and application objectives. We approach both problems by using language modeling on the crawled data only, omitting the need for manually validated language samples for training. On the task of discriminating between closely related languages we outperform the state-of-the-art Blacklist classifier reducing its error to a fourth.


Hierarchical Recurrent Neural Network for Document Modeling

Abstract: This paper proposes a novel hierarchical recurrent neural network language model (HRNNLM) for document modeling. After establishing a RNN to capture the coherence between sentences in a document, HRNNLM integrates it as the sentence history information into the word level RNN to predict the word sequence with cross-sentence contextual information. A two-step training approach is designed, in which sentence-level and word-level language models are approximated for the convergence in a pipeline style. Examined by the standard sentence reordering scenario, HRNNLM is proved for its better accuracy in modeling the sentence coherence. And at the word level, experimental results also indicate a significant lower model perplexity, followed by a practical better translation result when applied to a Chinese-English document translation reranking task.

document modeling
network language model
hierarchical recurrent
rnnlm


Unsupervised POS Induction with Word Embeddings

Abstract: Unsupervised word embeddings have been shown to be valuable as features in supervised learning problems; however, their role in unsupervised problems has been less thoroughly explored. In this paper, we show that embeddings can likewise add value to the problem of unsupervised POS induction. In two representative models of POS induction, we replace multinomial distributions over the vocabulary with multivariate Gaussian distributions over word embeddings and observe consistent improvements in eight languages. We also analyze the effect of various choices while inducing word embeddings on "downstream" POS induction results.

pos induction
word embedding
autoencoder


A Lexical Tool for Academic Writing in Spanish based on Expert and Novice Corpora

Abstract: The object of this article is to describe the extraction of data from a corpus of academic texts in Spanish and the use of those data for developing a lexical tool oriented to the production of academic texts. The corpus provides the lexical combinations that will be included in the afore-mentioned tool, namely collocations, idioms and formulas. They have been retrieved from the corpus controlling for their keyness (i.e., their specificity with regard to academic texts) and their even distribution across the corpus. For the extraction of collocations containing academic vocabulary other methods have been used, taking advantage of the morphological and syntactic information with which the corpus has been enriched. In the case of collocations and other multiword units, several association measures are being tested in order to restrict the list of candidates the lexicographers will have to deal with manually.


Automated Concatenation of Embeddings for Structured Prediction

Abstract: Pretrained contextualized embeddings are powerful word representations for structured prediction tasks. Recent work found that better word representations can be obtained by concatenating different types of embeddings. However, the selection of embeddings to form the best concatenated representation usually varies depending on the task and the collection of candidate embeddings, and the ever-increasing number of embedding types makes it a more difficult problem. In this paper, we propose Automated Concatenation of Embeddings (ACE) to automate the process of finding better concatenations of embeddings for structured prediction tasks, based on a formulation inspired by recent progress on neural architecture search. Specifically, a controller alternately samples a concatenation of embeddings, according to its current belief of the effectiveness of individual embedding types in consideration for a task, and updates the belief based on a reward. We follow strategies in reinforcement learning to optimize the parameters of the controller and compute the reward based on the accuracy of a task model, which is fed with the sampled concatenation as input and trained on a task dataset. Empirical results on 6 tasks and 21 datasets show that our approach outperforms strong baselines and achieves state-of-the-art performance with fine-tuned embeddings in all the evaluations.

embeddings
automated concatenation
ner task


Universal Dependencies v1: A Multilingual Treebank Collection

Abstract: Cross-linguistically consistent annotation is necessary for sound comparative evaluation and cross-lingual learning experiments. It is also useful for multilingual system development and comparative linguistic studies. Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. In this paper, we describe v1 of the universal guidelines, the underlying design principles, and the currently available treebanks for 33 languages.

treebank
universal dependencies
project
annotation guideline
morphological analysis


Translating Granularity of Event Slots into Features for Event Coreference Resolution.

Abstract: Using clues from event semantics to solve coreference, we present an “event template” approach to cross-document event coreference resolution on news articles. The approach uses a pairwise model, in which event information is compared along five semantically motivated slots of an event template. The templates, filled in on the sentence level for every event mention from the data set, are used for supervised classification. In this study, we determine granularity of events and we use the grain size as a clue for solving event coreference. We experiment with a newly-created granularity ontology employing granularity levels of locations, times and human participants as well as event durations as features in event coreference resolution. The granularity ontology is available for research. Results show that determining granularity along semantic event slots, even on the sentence level exclusively, improves precision and solves event coreference with scores comparable to those achieved in related work.

granularity
event slot
event coreference resolution


Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-Trained Language Models

Abstract: Recent works show that pre-trained language models (PTLMs), such as BERT, possess certain commonsense and factual knowledge. They suggest that it is promising to use PTLMs as “neural knowledge bases” via predicting masked words. Surprisingly, we find that this may not work for numerical commonsense knowledge (e.g., a bird usually has two legs). In this paper, we investigate whether and to what extent we can induce numerical commonsense knowledge from PTLMs as well as the robustness of this process. In this paper, we investigate whether and to what extent we can induce numerical commonsense knowledge from PTLMs as well as the robustness of this process. To study this, we introduce a novel probing task with a diagnostic dataset, NumerSense, containing 13.6k masked-word-prediction probes (10.5k for fine-tuning and 3.1k for testing). Our analysis reveals that: (1) BERT and its stronger variant RoBERTa perform poorly on the diagnostic dataset prior to any fine-tuning; (2) fine-tuning with distant supervision brings some improvement; (3) the best supervised model still performs poorly as compared to human performance (54.06% vs. 96.3% in accuracy).

numerical commonsense knowledge
pre-trained language model
bird


Siamese CBOW: Optimizing Word Embeddings for Sentence Representations

Abstract: We present the Siamese Continuous Bag of Words (Siamese CBOW) model, a neural network for efficient estimation of high-quality sentence embeddings. Averaging the embeddings of words in a sentence has proven to be a surprisingly successful and efficient way of obtaining sentence embeddings. However, word embeddings trained with the methods currently available are not optimized for the task of sentence representation, and, thus, likely to be suboptimal. Siamese CBOW handles this problem by training word embeddings directly for the purpose of being averaged. The underlying neural network learns word embeddings by predicting, from a sentence representation, its surrounding sentences. We show the robustness of the Siamese CBOW model by evaluating it on 20 datasets stemming from a wide variety of sources.


Selecting Proper Lexical Paraphrase for Children

Abstract: We propose a method for acquiring plain lexical paraphrase using a Japanese dictionary in order to achieve lexical simplification for children. The proposed method extracts plain words that are the most similar to the headword from the dictionary definition. The definition statements describe the headword using plain words; therefore, paraphrasing by replacing the headword with the most similar word in the dictionary definition is expected to be an accurate means of lexical simplification. However, it is difficult to determine which word is the most appropriate for the paraphrase. The method proposed in this paper measures the similarity of each word in the definition statements against the headword and selects the one with the closest semantic match for the paraphrase. This method compares favorably with the method that acquires the target word from the end of the definition statements.

lexical paraphrase
japanese dictionary
child


The perfect solution for detecting sarcasm in tweets #not

Abstract: To avoid a sarcastic message being understood in its unintended literal meaning, in microtexts such as messages on Twitter.com sarcasm is often explicitly marked with the hashtag ‘#sarcasm’. We collected a training corpus of about 78 thousand Dutch tweets with this hashtag. Assuming that the human labeling is correct (annotation of a sample indicates that about 85% of these tweets are indeed sarcastic), we train a machine learning classifier on the harvested examples, and apply it to a test set of a day’s stream of 3.3 million Dutch tweets. Of the 135 explicitly marked tweets on this day, we detect 101 (75%) when we remove the hashtag. We annotate the top of the ranked list of tweets most likely to be sarcastic that do not have the explicit hashtag. 30% of the top-250 ranked tweets are indeed sarcastic. Analysis shows that sarcasm is often signalled by hyperbole, using intensifiers and exclamations; in contrast, non-hyperbolic sarcastic messages often receive an explicit marker. We hypothesize that explicit markers such as hashtags are the digital extralinguistic equivalent of nonverbal expressions that people employ in live interaction when conveying sarcasm.


Answering Learners' Questions by Retrieving Question Paraphrases from Social Q&A Sites

Abstract: Information overload is a well-known problem which can be particularly detrimental to learners. In this paper, we propose a method to support learners in the information seeking process which consists in answering their questions by retrieving question paraphrases and their corresponding answers from social Q&A sites. Given the novelty of this kind of data, it is crucial to get a better understanding of how questions in social Q&A sites can be automatically analysed and retrieved. We discuss and evaluate several pre-processing strategies and question similarity metrics, using a new question paraphrase corpus collected from the WikiAnswers Q&A site. The results show that viable performance levels of more than 80% accuracy can be obtained for the task of question paraphrase retrieval.

question paraphrase
social q&a site
wikianswers repository


Do We Need Chinese Word Segmentation for Statistical Machine Translation?

Abstract: In Chinese texts, words are not separated by white spaces. This is problematic for many natural language processing tasks. The standard approach is to segment the Chinese character sequence into words. Here, we investigate Chinese word segmentation for statistical machine translation. We pursue two goals: the first one is the maximization of the final translation quality; the second is the minimization of the manual effort for building a translation system. The commonly used method for getting the word boundaries is based on a word segmentation tool and a predefined monolingual dictionary. To avoid the dependence of the translation system on an external dictionary, we have developed a system that learns a domainspecific dictionary from the parallel training corpus. This method produces results that are comparable with the predefined dictionary. Further more, our translation system is able to work without word segmentation with only a minor loss in translation quality.

word segmentation
chinese character
parallel training corpus


Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

Abstract: Language is increasingly being used to de-fine rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occurring labels and visual input but risk inadvertently encoding social biases found in web corpora. In this work, we study data and models associated with multilabel object classification and visual semantic role labeling. We find that (a) datasets for these tasks contain significant gender bias and (b) models trained on these datasets further amplify existing bias. For example, the activity cooking is over 33% more likely to involve females than males in a training set, and a trained model further amplifies the disparity to 68% at test time. We propose to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for collective inference. Our method results in almost no performance loss for the underlying recognition task but decreases the magnitude of bias amplification by 47.5% and 40.5% for multilabel classification and visual semantic role labeling, respectively。

gender bias
corpus-level constraint
semantic role labeling
man
social group


Lexical Normalisation of Short Text Messages: Makn Sens a #twitter

Abstract: Twitter provides access to large volumes of data in real time, but is notoriously noisy, hampering its utility for NLP. In this paper, we target out-of-vocabulary words in short text messages and propose a method for identifying and normalising ill-formed words. Our method uses a classifier to detect ill-formed words, and generates correction candidates based on morphophonemic similarity. Both word similarity and context are then exploited to select the most probable correction candidate for the word. The proposed method doesn't require any annotations, and achieves state-of-the-art performance over an SMS corpus and a novel dataset based on Twitter.

short text message
twitter
ill-formed word
lexical normalisation
normalization


Latent Predictor Networks for Code Generation

Abstract: Many language generation tasks require the production of text conditioned on both structured and unstructured inputs. We present a novel neural network architecture which generates an output sequence conditioned on an arbitrary number of input functions. Crucially, our approach allows both the choice of conditioning context and the granularity of generation, for example characters or tokens, to be marginalised, thus permitting scalable and effective training. Using this framework, we address the problem of generating programming code from a mixed natural language and structured specification. We create two new data sets for this paradigm derived from the collectible trading card games Magic the Gathering and Hearthstone. On these, and a third preexisting corpus, we demonstrate that marginalising multiple predictors allows our model to outperform strong benchmarks.

code generation
sequence-to-sequence model
java
python
programming language


Data Augmentation for Low-Resource Neural Machine Translation

Abstract: The quality of a Neural Machine Translation system depends substantially on the availability of sizable parallel corpora. For low-resource language pairs this is not the case, resulting in poor translation quality. Inspired by work in computer vision, we propose a novel data augmentation approach that targets low-frequency words by generating new sentence pairs containing rare words in new, synthetically created contexts. Experimental results on simulated low-resource settings show that our method improves translation quality by up to 2.9 BLEU points over the baseline and up to 3.2 BLEU over back-translation.

rare word
data augmentation
parallel training data
synthetic sentence pair


Error Analysis of Statistical Machine Translation Output

Abstract: Evaluation of automatic translation output is a difficult task. Several performance measures like Word Error Rate, Position Independent Word Error Rate and the BLEU and NIST scores are widely use and provide a useful tool for comparing different systems and to evaluate improvements within a system. However the interpretation of all of these measures is not at all clear, and the identification of the most prominent source of errors in a given system using these measures alone is not possible. Therefore some analysis of the generated translations is needed in order to identify the main problems and to focus the research efforts. This area is however mostly unexplored and few works have dealt with it until now. In this paper we will present a framework for classification of the errors of a machine translation system and we will carry out an error analysis of the system used by the RWTH in the first TC-STAR evaluation.

rwth
error analysis
typology
missing word
human evaluation


Leveraging Cognitive Features for Sentiment Analysis

Abstract: Sentiments expressed in user-generated short text and sentences are nuanced by subtleties at lexical, syntactic, semantic and pragmatic levels. To address this, we propose to augment traditional features used for sentiment analysis and sarcasm detection, with cognitive features derived from the eye-movement patterns of readers. Statistical classification using our enhanced feature set improves the performance (F-score) of polarity detection by a maximum of 3.7% and 9.3% on two datasets, over the systems that use only traditional features. We perform feature significance analysis, and experiment on a held-out dataset, showing that cognitive features indeed empower sentiment analyzers to handle complex constructs.

cognitive feature
sentiment analysis
sarcasm detection


Lessons from Natural Language Inference in the Clinical Domain

Abstract: State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training. This is even more challenging in specialized, and knowledge intensive domains, where training data is limited. To address this gap, we introduce MedNLI - a dataset annotated by doctors, performing a natural language inference task (NLI), grounded in the medical history of patients. We present strategies to: 1) leverage transfer learning using datasets from the open domain, (e.g. SNLI) and 2) incorporate domain knowledge from external data and lexical sources (e.g. medical terminologies). Our results demonstrate performance gains using both strategies.

natural language inference
clinical domain
mednli
history
open domain model


Learning First-Order Horn Clauses from Web Text

Abstract:
Even the entire Web corpus does not explicitly answer all questions, yet inference can uncover many implicit answers. But where do inference rules come from?

This paper investigates the problem of learning inference rules from Web text in an unsupervised, domain-independent manner. The Sherlock system, described herein, is a first-order learner that acquires over 30,000 Horn clauses from Web text. Sherlock embodies several innovations, including a novel rule scoring function based on Statistical Relevance (Salmon et al., 1971) which is effective on ambiguous, noisy and incomplete Web extractions. Our experiments show that inference over the learned rules discovers three times as many facts (at precision 0.8) as the TextRunner system which merely extracts facts explicitly stated in Web text.

horn clause
web text
inference rule
entailment rule


WIQA: A dataset for “What if...” reasoning over procedural text

Abstract: We introduce WIQA, the first large-scale dataset of “What if...” questions over procedural text. WIQA contains a collection of paragraphs, each annotated with multiple influence graphs describing how one change affects another, and a large (40k) collection of “What if...?” multiple-choice questions derived from these. For example, given a paragraph about beach erosion, would stormy weather hasten or decelerate erosion? WIQA contains three kinds of questions: perturbations to steps mentioned in the paragraph; external (out-of-paragraph) perturbations requiring commonsense knowledge; and irrelevant (no effect) perturbations. We find that state-of-the-art models achieve 73.8% accuracy, well below the human performance of 96.3%. We analyze the challenges, in particular tracking chains of influences, and present the dataset as an open challenge to the community.

reasoning
procedural text
large-scale dataset
wiqa


On Adversarial Examples for Character-Level Neural Machine Translation

Abstract: Evaluating on adversarial examples has become a standard procedure to measure robustness of deep learning models. Due to the difficulty of creating white-box adversarial examples for discrete text input, most analyses of the robustness of NLP models have been done through black-box adversarial examples. We investigate adversarial examples for character-level neural machine translation (NMT), and contrast black-box adversaries with a novel white-box adversary, which employs differentiable string-edit operations to rank adversarial changes. We propose two novel types of attacks which aim to remove or change a word in a translation, rather than simply break the NMT. We demonstrate that white-box adversarial examples are significantly stronger than their black-box counterparts in different attack scenarios, which show more serious vulnerabilities than previously known. In addition, after performing adversarial training, which takes only 3 times longer than regular training, we can improve the model's robustness significantly.

adversarial example
neural machine translation
perturbation
character-level nmt
token level


Selecting Syntactic, Non-redundant Segments in Active Learning for Machine Translation

Abstract: Active learning is a framework that makes it possible to efficiently train statistical models by selecting informative examples from a pool of unlabeled data. Previous work has found this framework effective for machine translation (MT), making it possible to train better translation models with less effort, particularly when annotators translate short phrases instead of full sentences. However, previous methods for phrase-based active learning in MT fail to consider whether the selected units are coherent and easy for human translators to translate, and also have problems with selecting redundant phrases with similar content. In this paper, we tackle these problems by proposing two new methods for selecting more syntactically coherent and less redundant segments in active learning for MT. Experiments using both simulation and extensive manual translation by professional translators find the proposed method effective, achieving both greater gain of BLEU score for the same number of translated words, and allowing translators to be more confident in their translations1.

segment
active learning
machine translation


The International Corpus of Arabic: Compilation, Analysis and Evaluation

Abstract: This paper focuses on a project for building the first International Corpus of Arabic (ICA). It is planned to contain 100 million analyzed tokens with an interface which allows users to interact with the corpus data in a number of ways [ICA website]. ICA is a representative corpus of Arabic that has been initiated in 2006, it is intended to cover the Modern Standard Arabic (MSA) language as being used all over the Arab world. ICA has been analyzed by Bibliotheca Alexandrina Morphological Analysis Enhancer (BAMAE). BAMAE is based on Buckwalter Arabic Morphological Analyzer (BAMA). Precision and Recall are the evaluation measures used to evaluate the BAMAE system. At this point, Precision measurement ranges from 95%-92% while recall measurement was 92%-89%. This depends on the number of qualifiers retrieved for every word. The percentages are expected to rise by implementing the improvements while working on larger amounts of data.


Additive Neural Networks for Statistical Machine Translation

Abstract: Most statistical machine translation (SMT) systems are modeled using a loglinear framework. Although the log-linear model achieves success in SMT, it still suffers from some limitations: (1) the features are required to be linear with respect to the model itself; (2) features cannot be further interpreted to reach their potential. A neural network is a reasonable method to address these pitfalls. However, modeling SMT with a neural network is not trivial, especially when taking the decoding efficiency into consideration. In this paper, we propose a variant of a neural network, i.e. additive neural networks, for SMT to go beyond the log-linear translation model. In addition, word embedding is employed as the input to the neural network, which encodes each word as a feature vector. Our model outperforms the log-linear translation models with/without embedding features on Chinese-to-English and Japanese-to-English translation tasks.

statistical machine translation
log-linear model
additive neural network


Fact Checking: Task definition and dataset construction

Abstract: In this paper we introduce the task of fact checking, i.e. the assessment of the truthfulness of a claim. The task is commonly performed manually by journalists verifying the claims made by public figures. Furthermore, ordinary citizens need to assess the truthfulness of the increasing volume of statements they consume. Thus, developing fact checking systems is likely to be of use to various members of society. We first define the task and detail the construction of a publicly available dataset using statements fact-checked by journalists available online. Then, we discuss baseline approaches for the task and the challenges that need to be addressed. Finally, we discuss how fact checking relates to mainstream natural language processing tasks and can stimulate further research.

fact checking
website
verdict
news detection
assignment


Syntactic Reordering for English-Arabic Phrase-Based Machine Translation

Abstract: We investigate syntactic reordering within an English to Arabic translation task. We extend a pre-translation syntactic reordering approach developed on a close language pair (English-Danish) to the distant language pair, English-Arabic. We achieve significant improvements in translation quality over related approaches, measured by manual as well as automatic evaluations. These results prove the viability of this approach for distant languages.

arabic
syntactic reordering
rich language


A Multi-Axis Annotation Scheme for Event Temporal Relations

Abstract: Existing temporal relation (TempRel) annotation schemes often have low inter-annotator agreements (IAA) even between experts, suggesting that the current annotation task needs a better definition. This paper proposes a new multi-axis modeling to better capture the temporal structure of events. In addition, we identify that event end-points are a major source of confusion in annotation, so we also propose to annotate TempRels based on start-points only. A pilot expert annotation effort using the proposed scheme shows significant improvement in IAA from the conventional 60's to 80's (Cohen's Kappa). This better-defined annotation scheme further enables the use of crowdsourcing to alleviate the labor intensity for each annotator. We hope that this work can foster more interesting studies towards event understanding.

annotation scheme
temporal relation
start-point


Image Description using Visual Dependency Representations

Abstract: Describing the main event of an image involves identifying the objects depicted and predicting the relationships between them. Previous approaches have represented images as unstructured bags of regions, which makes it difficult to accurately predict meaningful relationships between regions. In this paper, we introduce visual dependency representations to capture the relationships between the objects in an image, and hypothesize that this representation can improve image description. We test this hypothesis using a new data set of region-annotated images, associated with visual dependency representations and gold-standard descriptions. We describe two template-based description generation models that operate over visual dependency representations. In an image description task, we find that these models outperform approaches that rely on object proximity or corpus information to generate descriptions on both automatic measures and on human judgements.

visual dependency representation
image description
spatial relation
caption
preposition


Computational Argumentation Quality Assessment in Natural Language

Abstract: Research on computational argumentation faces the problem of how to automatically assess the quality of an argument or argumentation. While different quality dimensions have been approached in natural language processing, a common understanding of argumentation quality is still missing. This paper presents the first holistic work on computational argumentation quality in natural language. We comprehensively survey the diverse existing theories and approaches to assess logical, rhetorical, and dialectical quality dimensions, and we derive a systematic taxonomy from these. In addition, we provide a corpus with 320 arguments, annotated for all 15 dimensions in the taxonomy. Our results establish a common ground for research on computational argumentation quality assessment.

argumentation
dialectical quality dimension
taxonomy
reasonableness


Sarcasm Analysis Using Conversation Context

Abstract: Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, the speaker's sarcastic intent is not always apparent without additional context. Focusing on social media discussions, we investigate three issues: (1) does modeling conversation context help in sarcasm detection? (2) can we identify what part of conversation context triggered the sarcastic reply? and (3) given a sarcastic post that contains multiple sentences, can we identify the specific sentence that is sarcastic? To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the current turn. We show that LSTM networks with sentence-level attention on context and current turn, as well as the conditional LSTM network, outperform the LSTM model that reads only the current turn. As conversation context, we consider the prior turn, the succeeding turn, or both. Our computational models are tested on two types of social media platforms: Twitter and discussion forums. We discuss several differences between these data sets, ranging from their size to the nature of the gold-label annotations. To address the latter two issues, we present a qualitative analysis of the attention weights produced by the LSTM models (with attention) and discuss the results compared with human performance on the two tasks.

conversation context
sentence-level attention
sarcasm analysis


Tracking State Changes in Procedural Text: a Challenge Dataset and Models for Process Paragraph Comprehension

Abstract: We present a new dataset and models for comprehending paragraphs about processes (e.g., photosynthesis), an important genre of text describing a dynamic world. The new dataset, ProPara, is the first to contain natural (rather than machine-generated) text about a changing world along with a full annotation of entity states (location and existence) during those changes (81k datapoints). The end-task, tracking the location and existence of entities through the text, is challenging because the causal effects of actions are often implicit and need to be inferred. We find that previous models that have worked well on synthetic data achieve only mediocre performance on ProPara, and introduce two new neural models that exploit alternative mechanisms for state prediction, in particular using LSTM input encoding and span prediction. The new models improve accuracy by up to 19%. We are releasing the ProPara dataset and our models to the community.

state change
procedural text
paragraph
scientific process


Enhancing BERT for Lexical Normalization

Abstract: Language model-based pre-trained representations have become ubiquitous in natural language processing. They have been shown to significantly improve the performance of neural models on a great variety of tasks. However, it remains unclear how useful those general models can be in handling non-canonical text. In this article, focusing on User Generated Content (UGC), we study the ability of BERT to perform lexical normalisation. Our contribution is simple: by framing lexical normalisation as a token prediction task, by enhancing its architecture and by carefully fine-tuning it, we show that BERT can be a competitive lexical normalisation model without the need of any UGC resources aside from 3,000 training sentences. To the best of our knowledge, it is the first work done in adapting and analysing the ability of this model to handle noisy UGC data.


Modeling discourse cohesion for discourse parsing via memory network

Abstract: Identifying long-span dependencies between discourse units is crucial to improve discourse parsing performance. Most existing approaches design sophisticated features or exploit various off-the-shelf tools, but achieve little success. In this paper, we propose a new transition-based discourse parser that makes use of memory networks to take discourse cohesion into account. The automatically captured discourse cohesion benefits discourse parsing, especially for long span scenarios. Experiments on the RST discourse treebank show that our method outperforms traditional featured based methods, and the memory based discourse cohesion can improve the overall parsing performance significantly.

discourse cohesion
memory network
transition-based discourse parser


A Composite Kernel Approach for Dialog Topic Tracking with Structured Domain Knowledge from Wikipedia

Abstract: Dialog topic tracking aims at analyzing and maintaining topic transitions in ongoing dialogs. This paper proposes a composite kernel approach for dialog topic tracking to utilize various types of domain knowledge obtained from Wikipedia. Two kernels are defined based on history sequences and context trees constructed based on the extracted features. The experimental results show that our composite kernel approach can significantly improve the performances of topic tracking in mixed-initiative human-human dialogs.

dialog topic tracking
domain knowledge
wikipedia


Induction Networks for Few-Shot Text Classification

Abstract: Text classification tends to struggle when data is deficient or when it needs to adapt to unseen classes. In such challenging scenarios, recent studies have used meta-learning to simulate the few-shot task, in which new queries are compared to a small support set at the sample-wise level. However, this sample-wise comparison may be severely disturbed by the various expressions in the same class. Therefore, we should be able to learn a general representation of each class in the support set and then compare it to new queries. In this paper, we propose a novel Induction Network to learn such a generalized class-wise representation, by innovatively leveraging the dynamic routing algorithm in meta-learning. In this way, we find the model is able to induce and generalize better. We evaluate the proposed model on a well-studied sentiment classification dataset (English) and a real-world dialogue intent classification dataset (Chinese). Experiment results show that on both datasets, the proposed model significantly outperforms the existing state-of-the-art approaches, proving the effectiveness of class-wise generalization in few-shot text classification.

text classification
induction networks
few-shot learning
new class
nlp community


Scalable Inference and Training of Context-Rich Syntactic Translation Models

Abstract: Statistical MT has made great progress in the last few years, but current translation models are weak on re-ordering and target language fluency. Syntactic approaches seek to remedy these problems. In this paper, we take the framework for acquiring multi-level syntactic translation rules of (Galley et al., 2004) from aligned tree-string pairs, and present two main extensions of their approach: first, instead of merely computing a single derivation that minimally explains a sentence pair, we construct a large number of derivations that include contextually richer rules, and account for multiple interpretations of unaligned words. Second, we propose probability estimates and a training procedure for weighting these rules. We contrast different approaches on real examples, show that our estimates based on multiple derivations favor phrasal re-orderings that are linguistically better motivated, and establish that our larger rules provide a 3.63 BLEU point increase over minimal rules.

translation model
smt system
syntax-based model
string-to-tree model
artificial constituent node


Few-Shot Charge Prediction with Discriminative Legal Attributes

Abstract: Automatic charge prediction aims to predict the final charges according to the fact descriptions in criminal cases and plays a crucial role in legal assistant systems. Existing works on charge prediction perform adequately on those high-frequency charges but are not yet capable of predicting few-shot charges with limited cases. Moreover, these exist many confusing charge pairs, whose fact descriptions are fairly similar to each other. To address these issues, we introduce several discriminative attributes of charges as the internal mapping between fact descriptions and charges. These attributes provide additional information for few-shot charges, as well as effective signals for distinguishing confusing charges. More specifically, we propose an attribute-attentive charge prediction model to infer the attributes and charges simultaneously. Experimental results on real-work datasets demonstrate that our proposed model achieves significant and consistent improvements than other state-of-the-art baselines. Specifically, our model outperforms other baselines by more than 50% in the few-shot scenario. Our codes and datasets can be obtained from <https://github.com/thunlp/attribute_charge>.

charge prediction
legal attribute
discriminative attribute


Learning to Summarize Radiology Findings

Abstract: The Impression section of a radiology report summarizes crucial radiology findings in natural language and plays a central role in communicating these findings to physicians. However, the process of generating impressions by summarizing findings is time-consuming for radiologists and prone to errors. We propose to automate the generation of radiology impressions with neural sequence-to-sequence learning. We further propose a customized neural model for this task which learns to encode the study background information and use this information to guide the decoding process. On a large dataset of radiology reports collected from actual hospital studies, our model outperforms existing non-neural and neural baselines under the ROUGE metrics. In a blind experiment, a board-certified radiologist indicated that 67% of sampled system summaries are at least as good as the corresponding human-written summaries, suggesting significant clinical validity. To our knowledge our work represents the first attempt in this direction.

summarization
radiology finding
reference
medical knowledge


COMET: A Neural Framework for MT Evaluation

Abstract: We present COMET, a neural framework for training multilingual machine translation evaluation models which obtains new state-of-the-art levels of correlation with human judgements. Our framework leverages recent breakthroughs in cross-lingual pretrained language modeling resulting in highly multilingual and adaptable MT evaluation models that exploit information from both the source input and a target-language reference translation in order to more accurately predict MT quality. To showcase our framework, we train three models with different types of human judgements: Direct Assessments, Human-mediated Translation Edit Rate and Multidimensional Quality Metric. Our models achieve new state-of-the-art performance on the WMT 2019 Metrics shared task and demonstrate robustness to high-performing systems.

neural framework
reference
comet
evaluation metric
bertscore


Rethinking Perturbations in Encoder-Decoders for Fast Training

Abstract: We often use perturbations to regularize neural models. For neural encoder-decoders, previous studies applied the scheduled sampling (Bengio et al., 2015) and adversarial perturbations (Sato et al., 2019) as perturbations but these methods require considerable computational time. Thus, this study addresses the question of whether these approaches are efficient enough for training time. We compare several perturbations in sequence-to-sequence problems with respect to computational time. Experimental results show that the simple techniques such as word dropout (Gal and Ghahramani, 2016) and random replacement of input tokens achieve comparable (or better) scores to the recently proposed perturbations, even though these simple methods are faster.

perturbation
encoder-decoder
word dropout


Transition-Based Dependency Parsing with Stack Long Short-Term Memory

Abstract: This work was sponsored in part by the U. S. Army Research Laboratory and the U. S. Army Research Office/nunder contract/grant number W911NF-10-1-0533, and in part by NSF CAREER grant IIS-1054319./nMiguel Ballesteros is supported by the European Commission under the contract numbers FP7-ICT-610411 (project MULTISENSOR) and H2020-RIA-645012 (project KRISTINA).

dependency parsing
stack lstm
pos tag
short-term memory network
transition-based method


Event Linking: Grounding Event Reference in a News Archive

Abstract: Interpreting news requires identifying its constituent events. Events are complex linguistically and ontologically, so disambiguating their reference is challenging. We introduce event linking, which canonically labels an event reference with the article where it was first reported. This implicitly relaxes coreference to co-reporting, and will practically enable augmenting news archives with semantic hyperlinks. We annotate and analyse a corpus of 150 documents, extracting 501 links to a news archive with reasonable inter-annotator agreement.

event reference
coreference
news archive
inter-annotator agreement


Improving Translation Quality by Discarding Most of the Phrasetable

Abstract: It is possible to reduce the bulk of phrasetables for Statistical Machine Translation using a technique based on the significance testing of phrase pair co-occurrence in the parallel corpus. The savings can be quite substantial (up to 90%) and cause no reduction in BLEU score. In some cases, an improvement in BLEU is obtained at the same time although the effect is less pronounced if state-of-the-art phrasetable smoothing is employed.

translation quality
significance testing
bleu score
phrase table


A Topic Model for Word Sense Disambiguation

Abstract: We develop latent Dirichlet allocation with WORDNET (LDAWN), an unsupervised probabilistic topic model that includes word sense as a hidden variable. We develop a probabilistic posterior inference algorithm for simultaneously disambiguating a corpus and learning the domains in which to consider each word. Using the WORDNET hierarchy, we embed the construction of Abney and Light (1999) in the topic model and show that automatically learned domains improve WSD accuracy compared to alternative contexts.

topic model
word sense disambiguation
ldawn


Metaphor Detection with Cross-Lingual Model Transfer

Abstract: We show that it is possible to reliably discriminate whether a syntactic construction is meant literally or metaphorically using lexical semantic features of the words that participate in the construction. Our model is constructed using English resources, and we obtain state-of-the-art performance relative to previous work in this language. Using a model transfer approach by pivoting through a bilingual dictionary, we show our model can identify metaphoric expressions in other languages. We provide results on three new test sets in Spanish, Farsi, and Russian. The results support the hypothesis that metaphors are conceptual, rather than lexical, in nature.

cross-lingual model transfer
metaphor detection
abstractness
sentiment analysis
language processing application


Findings of the 2016 Conference on Machine Translation

Abstract: This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments). The quality estimation task had three subtasks, with a total of 14 teams, submitting 39 entries. The automatic post-editing task had a total of 6 teams, submitting 11 entries.

conference
machine translation
automatic post-editing task
online system
state-of-the-art result


Controlling Output Length in Neural Encoder-Decoders

Abstract: Neural encoder-decoder models have shown great success in many sequence generation tasks. However, previous work has not investigated situations in which we would like to control the length of encoder-decoder outputs. This capability is crucial for applications such as text summarization, in which we have to generate concise summaries with a desired length. In this paper, we propose methods for controlling the output sequence length for neural encoder-decoder models: two decoding-based methods and two learning-based methods. Results show that our learning-based methods have the capability to control length without degrading summary quality in a summarization task.

output length
encoder-decoder model
sentence compression


On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models

Abstract: We consider two graph models of semantic change. The first is a time-series model that relates embedding vectors from one time period to embedding vectors of previous time periods. In the second, we construct one graph for each word: nodes in this graph correspond to time points and edge weights to the similarity of the word’s meaning across two time points. We apply our two models to corpora across three different languages. We find that semantic change is linear in two senses. Firstly, today’s embedding vectors (= meaning) of words can be derived as linear combinations of embedding vectors of their neighbors in previous time periods. Secondly, self-similarity of words decays linearly in time. We consider both findings as new laws/hypotheses of semantic change.

semantic change
time period
self-similarity


Dependency Annotation Scheme for Indian Languages

Abstract: The paper introduces a dependency annotation effort which aims to fully annotate a million word Hindi corpus. It is the first attempt of its kind to develop a large scale tree-bank for an Indian language. In this paper we provide the motivation for following the Paninian framework as the annotation scheme and argue that the Paninian framework is better suited to model the various linguistic phenomena manifest in Indian languages. We present the basic annotation scheme. We also show how the scheme handles some phenomenon such as complex verbs, ellipses, etc. Empirical results of some experiments done on the currently annotated sentences are also reported.

indian language
paninian framework
dependency annotation scheme


Using paraphrases for improving first story detection in news and Twitter

Abstract: First story detection (FSD) involves identifying first stories about events from a continuous stream of documents. A major problem in this task is the high degree of lexical variation in documents which makes it very difficult to detect stories that talk about the same event but expressed using different words. We suggest using paraphrases to alleviate this problem, making this the first work to use paraphrases for FSD. We show a novel way of integrating paraphrases with locality sensitive hashing (LSH) in order to obtain an efficient FSD system that can scale to very large datasets. Our system achieves state-of-the-art results on the first story detection task, beating both the best supervised and unsupervised systems. To test our approach on large data, we construct a corpus of events for Twitter, consisting of 50 million documents, and show that paraphrasing is also beneficial in this domain.


SciBERT: A Pretrained Language Model for Scientific Text

Abstract: Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et. al., 2018) to address the lack of high-quality, large-scale labeled scientific data. SciBERT leverages unsupervised pretraining on a large multi-domain corpus of scientific publications to improve performance on downstream scientific NLP tasks. We evaluate on a suite of tasks including sequence tagging, sentence classification and dependency parsing, with datasets from a variety of scientific domains. We demonstrate statistically significant improvements over BERT and achieve new state-of-the-art results on several of these tasks. The code and pretrained models are available at <https://github.com/allenai/scibert/>.

language model
scientific text
downstream task
biomedical domain
pre-trained model


Compositional Semantic Parsing across Graphbanks

Abstract: Most semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. We present a compositional neural semantic parser which achieves, for the first time, competitive accuracies across a diverse range of graphbanks. Incorporating BERT embeddings and multi-task learning improves the accuracy further, setting new states of the art on DM, PAS, PSD, AMR 2015 and EDS.


Kernel-Based Pronoun Resolution with Structured Syntactic Knowledge

Abstract: Syntactic knowledge is important for pronoun resolution. Traditionally, the syntactic information for pronoun resolution is represented in terms of features that have to be selected and defined heuristically. In the paper, we propose a kernel-based method that can automatically mine the syntactic information from the parse trees for pronoun resolution. Specifically, we utilize the parse trees directly as a structured feature and apply kernel functions to this feature, as well as other normal features, to learn the resolution classifier. In this way, our approach avoids the efforts of decoding the parse trees into the set of flat syntactic features. The experimental results show that our approach can bring significant performance improvement and is reliably effective for the pronoun resolution task.

pronoun resolution
syntactic knowledge
convolution tree kernel


Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation

Abstract: Contemporary machine translation systems achieve greater coverage by applying subword models such as BPE and character-level CNNs, but these methods are highly sensitive to orthographical variations such as spelling mistakes. We show how training on a mild amount of random synthetic noise can dramatically improve robustness to these variations, without diminishing performance on clean text. We focus on translation performance on natural typos, and show that robustness to such noise can be achieved using a balanced diet of simple synthetic noises at training time, without access to the natural noise data or distribution.

synthetic noise
robustness
machine translation
training sample
adversarial example


Strong Baselines for Neural Semi-Supervised Learning under Domain Shift

Abstract: Novel neural models have been proposed in recent years for learning under domain shift. Most models, however, only evaluate on a single task, on proprietary datasets, or compare to weak baselines, which makes comparison of models difficult. In this paper, we re-evaluate classic general-purpose bootstrapping approaches in the context of neural networks under domain shifts vs. recent neural approaches and propose a novel multi-task tri-training method that reduces the time and space complexity of classic tri-training. Extensive experiments on two benchmarks for part-of-speech tagging and sentiment analysis are negative: while our novel method establishes a new state-of-the-art for sentiment analysis, it does not fare consistently the best. More importantly, we arrive at the somewhat surprising conclusion that classic tri-training, with some additions, outperforms the state-of-the-art for NLP. Hence classic approaches constitute an important and strong baseline.

domain shift
tri-training
strong baseline


Weakly Supervised Training of Semantic Parsers

Abstract: We present a method for training a semantic parser using only a knowledge base and an unlabeled text corpus, without any individually annotated sentences. Our key observation is that multiple forms of weak supervision can be combined to train an accurate semantic parser: semantic supervision from a knowledge base, and syntactic supervision from dependency-parsed sentences. We apply our approach to train a semantic parser that uses 77 relations from Freebase in its knowledge representation. This semantic parser extracts instances of binary relations with state-of-the-art accuracy, while simultaneously recovering much richer semantic structures, such as conjunctions of multiple relations with partially shared arguments. We demonstrate recovery of this richer structure by extracting logical forms from natural language queries against Freebase. On this task, the trained semantic parser achieves 80% precision and 56% recall, despite never having seen an annotated logical form.

semantic parser
knowledge base
freebase
distant supervision


Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches

Abstract: Sentences in a well-formed text are connected to each other via various links to form the cohesive structure of the text. Current neural machine translation (NMT) systems translate a text in a conventional sentence-by-sentence fashion, ignoring such cross-sentence links and dependencies. This may lead to generate an incoherent target text for a coherent source text. In order to handle this issue, we propose a cache-based approach to modeling coherence for neural machine translation by capturing contextual information either from recently translated sentences or the entire document. Particularly, we explore two types of caches: a dynamic cache, which stores words from the best translation hypotheses of preceding sentences, and a topic cache, which maintains a set of target-side topical words that are semantically related to the document to be translated. On this basis, we build a new layer to score target words in these two caches with a cache-based neural model. Here the estimated probabilities from the cache-based neural model are combined with NMT probabilities into the final word prediction probabilities via a gating mechanism. Finally, the proposed cache-based neural model is trained jointly with NMT system in an end-to-end manner. Experiments and analysis presented in this paper demonstrate that the proposed cache-based model achieves substantial improvements over several state-of-the-art SMT and NMT baselines.

coherence
neural machine translation
topic cache
translation quality


A Shared Task on Bandit Learning for Machine Translation

Abstract: We introduce and describe the results of a novel shared task on bandit learning for machine translation. The task was organized jointly by Amazon and Heidelberg University for the first time at the Second Conference on Machine Translation (WMT 2017). The goal of the task is to encourage research on learning machine translation from weak user feedback instead of human references or post-edits. On each of a sequence of rounds, a machine translation system is required to propose a translation for an input, and receives a real-valued estimate of the quality of the proposed translation for learning. This paper describes the shared task's learning and evaluation setup, using services hosted on Amazon Web Services (AWS), the data and evaluation metrics, and the results of various machine translation architectures and learning protocols.

bandit learning
machine translation
feedback
post-edit


What Makes Good In-Context Examples for GPT-3?

Abstract: GPT-3 has attracted lots of attention due to its superior performance across a wide range of NLP tasks, especially with its in-context learning abilities. Despite its success, we found that the empirical results of GPT-3 depend heavily on the choice of in-context examples. In this work, we investigate whether there are more effective strategies for judiciously selecting in-context examples (relative to random sampling) that better leverage GPT-3's in-context learning capabilities. Inspired by the recent success of leveraging a retrieval module to augment neural networks, we propose to retrieve examples that are semantically-similar to a test query sample to formulate its corresponding prompt. Intuitively, the examples selected with such a strategy may serve as more informative inputs to unleash GPT-3's power of text generation. We evaluate the proposed approach on several natural language understanding and generation benchmarks, where the retrieval-based prompt selection approach consistently outperforms the random selection baseline. Moreover, it is observed that the sentence encoders fine-tuned on task-related datasets yield even more helpful retrieval results. Notably, significant gains are observed on tasks such as table-to-text generation (44.3% on the ToTTo dataset) and open-domain question answering (45.5% on the NQ dataset).

in-context example
gpt-3
few-shot learning


Reflections on the Penn Discourse TreeBank, Comparable Corpora, and Complementary Annotation

Abstract: The Penn Discourse Treebank (PDTB) was released to the public in 2008. It remains the largest manually annotated corpus of discourse relations to date. Its focus on discourse relations that are either lexically-grounded in explicit discourse connectives or associated with sentential adjacency has not only facilitated its use in language technology and psycholinguistics but also has spawned the annotation of comparable corpora in other languages and genres.Given this situation, this paper has four aims: (1) to provide a comprehensive introduction to the PDTB for those who are unfamiliar with it; (2) to correct some wrong (or perhaps inadvertent) assumptions about the PDTB and its annotation that may have weakened previous results or the performance of decision procedures induced from the data; (3) to explain variations seen in the annotation of comparable resources in other languages and genres, which should allow developers of future comparable resources to recognize whether the variations are relevant to them; and (4) to enumerate and explain relationships between PDTB annotation and complementary annotation of other linguistic phenomena. The paper draws on work done by ourselves and others since the corpus was released.

penn discourse treebank
comparable corpora
annotator
pdtb annotation


Orthographic and Morphological Processing for English-Arabic Statistical Machine Translation

Abstract: Much of the work on Statistical Machine Translation (SMT) from morphologically rich languages has shown that morphological tokenization and orthographic normalization help improve SMT quality because of the sparsity reduction they contribute. In this paper, we study the effect of these processes on SMT when translating into a morphologically rich language, namely Arabic. We explore a space of tokenization schemes and normalization options. We only evaluate on detokenized and orthographically correct (enriched) output. Our results show that the best performing tokenization scheme is that of the Penn Arabic Treebank. Additionally, training on orthographically normalized (reduced) text then jointly enriching and detokenizing the output outperforms training on enriched text.

statistical machine translation
rich language
tokenization
arabic


Discriminative Reranking for Natural Language Parsing

Abstract: This article considers approaches which rerank the output of an existing probabilistic parser. The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. A second model then attempts to improve upon this initial ranking, using additional features of the tree as evidence. The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes these features into account. We introduce a new method for the reranking task, based on the boosting approach to ranking problems described in Freund et al. (1998). We apply the boosting method to parsing the Wall Street Journal treebank. The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features over parse trees that were not included in the original model. The new model achieved 89.75 F-measure, a 13 relative decrease in F-measure error over the baseline model's score of 88.2. The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. Experiments show significant efficiency gains for the new algorithm over the obvious implementation of the boosting approach. We argue that the method is an appealing alternative-in terms of both simplicity and efficiency-to work on feature selection methods within log-linear (maximum-entropy) models. Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, or natural language generation.

generative model
collins
feature space
discriminative reranking
syntactic parsing


Odin's Runes: A Rule Language for Information Extraction

Abstract: Odin is an information extraction framework that applies cascades of finite state automata over both surface text and syntactic dependency graphs. Support for syntactic patterns allow us to concisely define relations that are otherwise difficult to express in languages such as Common Pattern Specification Language (CPSL), which are currently limited to shallow linguistic features. The interaction of lexical and syntactic automata provides robustness and flexibility when writing extraction rules. This paper describes Odin's declarative language for writing these cascaded automata.

rule language
information extraction framework
odin


Adapting Term Recognition to an Under-Resourced Language: the Case of Irish

Abstract: Automatic Term Recognition (ATR) is an important method for the summarization and analysis of large corpora, and normally requires a significant amount of linguistic input, in particular the use of part-ofspeech taggers. For an under-resourced language such as Irish, the resources necessary for this may be scarce or entirely absent. We evaluate two methods for the automatic extraction of terms, based on the small part-of-speech-tagged corpora that are available for Irish and on a large terminology list, and show that both methods can produce viable term extractors. We evaluate this with a newly constructed corpus that is the first available corpus for term extraction in Irish. Our results shine some light on the challenge of adapting natural language processing systems to under-resourced scenarios.

term recognition
under-resourced language
irish


BiTAM: Bilingual Topic AdMixture Models for Word Alignment

Abstract: We propose a novel bilingual topical admixture (BiTAM) formalism for word alignment in statistical machine translation. Under this formalism, the parallel sentence-pairs within a document-pair are assumed to constitute a mixture of hidden topics; each word-pair follows a topic-specific bilingual translation model. Three BiTAM models are proposed to capture topic sharing at different levels of linguistic granularity (i.e., at the sentence or word levels). These models enable word-alignment process to leverage topical contents of document-pairs. Efficient variational approximation algorithms are designed for inference and parameter estimation. With the inferred latent topics, BiTAM models facilitate coherent pairing of bilingual linguistic entities that share common topical aspects. Our preliminary experiments show that the proposed models improve word alignment accuracy, and lead to better translation quality.

word alignment
mixture
hidden topic
different level
topical content


Parser Combination by Reparsing

Abstract: We present a novel parser combination scheme that works by reparsing input sentences once they have already been parsed by several different parsers. We apply this idea to dependency and constituent parsing, generating results that surpass state-of-the-art accuracy levels for individual parsers.

reparsing
parser combination
dependency graph


A Web Survey on the Use of Active Learning to Support Annotation of Text Data

Abstract: As supervised machine learning methods for addressing tasks in natural language processing (NLP) prove increasingly viable, the focus of attention is naturally shifted towards the creation of training data. The manual annotation of corpora is a tedious and time consuming process. To obtain high-quality annotated data constitutes a bottleneck in machine learning for NLP today. Active learning is one way of easing the burden of annotation. This paper presents a first probe into the NLP research community concerning the nature of the annotation projects undertaken in general, and the use of active learning as annotation support in particular.

web survey
active learning
annotation project


The CoNLL-2013 Shared Task on Grammatical Error Correction

Abstract: The CoNLL-2013 shared task was devoted to grammatical error correction. In this paper, we give the task definition, present the data sets, and describe the evaluation metric and scorer used in the shared task. We also give an overview of the various approaches adopted by the participating teams, and present the evaluation results.

conll-2013
grammatical error correction
learner
content word
punctuation


Modeling Localness for Self-Attention Networks

Abstract: Self-attention networks have proven to be of profound value for its strength of capturing global dependencies. In this work, we propose to model localness for self-attention networks, which enhances the ability of capturing useful local context. We cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention. The bias is then incorporated into the original attention distribution to form a revised distribution. To maintain the strength of capturing long distance dependencies while enhance the ability of capturing short-range dependencies, we only apply localness modeling to lower layers of self-attention networks. Quantitative and qualitative analyses on Chinese-English and English-German translation tasks demonstrate the effectiveness and universality of the proposed approach.

localness
self-attention network
gaussian bias
scope
translation task


Learning to Automatically Solve Algebra Word Problems

Abstract: We present an approach for automatically learning to solve algebra word problems. Our algorithm reasons across sentence boundaries to construct and solve a system of linear equations, while simultaneously recovering an alignment of the variables and numbers in these equations to the problem text. The learning algorithm uses varied supervision, including either full equations or just the final answers. We evaluate performance on a newly gathered corpus of algebra word problems, demonstrating that the system can correctly answer almost 70% of the questions in the dataset. This is, to our knowledge, the first learning result for this task.

algebra word problem
operation
hand-crafted feature
learning method
mwp solver


Learning Word Vectors for 157 Languages

Abstract: Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train them on very large corpora, and use these pre-trained models in downstream tasks. In this paper, we describe how we trained such high quality word representations for 157 languages. We used two sources of data to train these models: the free online encyclopedia Wikipedia and data from the common crawl project. We also introduce three new word analogy datasets to evaluate these word vectors, for French, Hindi and Polish. Finally, we evaluate our pre-trained word vectors on 10 languages for which evaluation datasets exists, showing very strong performance compared to previous models.

word vector
pre-trained model
wikipedia
hindi
fasttext


TESLA: Translation Evaluation of Sentences with Linear-Programming-Based Analysis

Abstract: We present TESLA-M and TESLA, two novel automatic machine translation evaluation metrics with state-of-the-art performances. TESLA-M builds on the success of METEOR and MaxSim, but employs a more expressive linear programming framework. TESLA further exploits parallel texts to build a shallow semantic representation. We evaluate both on the WMT 2009 shared evaluation task and show that they outperform all participating systems in most tasks.


SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation

Abstract: Comunicacio presentada al 10th International Workshop on Semantic Evaluation (SemEval-2016), celebrat els dies 16 i 17 de juny de 2016 a San Diego, California.

semantic textual similarity
semeval
cross-lingual sub-task


FloDusTA: Saudi Tweets Dataset for Flood, Dust Storm, and Traffic Accident Events

Abstract: The rise of social media platforms makes it a valuable information source of recent events and users' perspective towards them. Twitter has been one of the most important communication platforms in recent years. Event detection, one of the information extraction aspects, involves identifying specified types of events in the text. Detecting events from tweets can help to predict real-world events precisely. A serious challenge that faces Arabic event detection is the lack of Arabic datasets that can be exploited in detecting events. This paper will describe FloDusTA, which is a dataset of tweets that we have built for the purpose of developing an event detection system. The dataset contains tweets written in both Modern Standard Arabic and Saudi dialect. The process of building the dataset starting from tweets collection to annotation by human annotators will be present. The tweets are labeled with four labels: flood, dust storm, traffic accident, and non-event. The dataset was tested for classification and the result was strongly encouraging.


CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

Abstract: The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, the task was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.

shared task
raw text
universal dependency


Cross-Linguistic Sentiment Analysis: From English to Spanish

Abstract: We explore the adaptation of English resources and techniques for text sentiment analysis to a new language, Spanish. Our main focus is the modification of an existing English semantic orientation calculator and the building of dictionaries; however we also compare alternate approaches, including machine translation and Support Vector Machine classification. The results indicate that, although languageindependent methods provide a decent baseline performance, there is also a significant cost to automation, and thus the best path to long-term improvement is through the inclusion of language-specific knowledge and resources.

sentiment analysis
spanish
machine translation


PHINC: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation

Abstract: Code-mixing is the phenomenon of using more than one language in a sentence. In the multilingual communities, it is a very frequently observed pattern of communication on social media platforms. Flexibility to use multiple languages in one text message might help to communicate efficiently with the target audience. But, the noisy user-generated code-mixed text adds to the challenge of processing and understanding natural language to a much larger extent. Machine translation from monolingual source to the target language is a well-studied research problem. Here, we demonstrate that widely popular and sophisticated translation systems such as Google Translate fail at times to translate code-mixed text effectively. To address this challenge, we present a parallel corpus of the 13,738 code-mixed Hindi-English sentences and their corresponding human translation in English. In addition, we also propose a translation pipeline build on top of Google Translate. The evaluation of the proposed pipeline on PHINC demonstrates an increase in the performance of the underlying system. With minimal effort, we can extend the dataset and the proposed approach to other code-mixing language pairs.

machine translation
code-mixed language
phinc


Learning Accurate, Compact, and Interpretable Tree Annotation

Abstract: We present an automatic approach to tree annotation in which basic nonterminal symbols are alternately split and merged to maximize the likelihood of a training treebank. Starting with a simple X-bar grammar, we learn a new grammar whose nonterminals are subsymbols of the original nonterminals. In contrast with previous work, we are able to split various terminals to different degrees, as appropriate to the actual complexity in the data. Our grammars automatically learn the kinds of linguistic distinctions exhibited in previous work on manual tree annotation. On the other hand, our grammars are much more compact and substantially more accurate than previous work on automatic annotation. Despite its simplicity, our best grammar achieves an F1 of 90.2% on the Penn Treebank, higher than fully lexicalized systems.

berkeley parser
latent annotation
pcfg
variable approach
subcategorie


A Conventional Orthography for Tunisian Arabic

Abstract: Tunisian Arabic is a dialect of the Arabic language spoken in Tunisia. Tunisian Arabic is an under-resourced language. It has neither a standard orthography nor large collections of written text and dictionaries. Actually, there is no strict separation between Modern Standard Arabic, the official language of the government, media and education, and Tunisian Arabic; the two exist on a continuum dominated by mixed forms. In this paper, we present a conventional orthography for Tunisian Arabic, following a previous effort on developing a conventional orthography for Dialectal Arabic (or CODA) demonstrated for Egyptian Arabic. We explain the design principles of CODA and provide a detailed description of its guidelines as applied to Tunisian Arabic.

conventional orthography
tunisian arabic
dialect


Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning

Abstract: Implicit discourse relation recognition aims to understand and annotate the latent relations between two discourse arguments, such as temporal, comparison, etc. Most previous methods encode two discourse arguments separately, the ones considering pair specific clues ignore the bidirectional interactions between two arguments and the sparsity of pair patterns. In this paper, we propose a novel neural Tensor network framework with Interactive Attention and Sparse Learning (TIASL) for implicit discourse relation recognition. (1) We mine the most correlated word pairs from two discourse arguments to model pair specific clues, and integrate them as interactive attention into argument representations produced by the bidirectional long short-term memory network. Meanwhile, (2) the neural tensor network with sparse constraint is proposed to explore the deeper and the more important pair patterns so as to fully recognize discourse relations. The experimental results on PDTB show that our proposed TIASL framework is effective.

neural tensor network
discourse argument
word pair


BERTweet: A pre-trained language model for English Tweets

Abstract: We present BERTweet, the first public large-scale pre-trained language model for English Tweets. Our BERTweet, having the same architecture as BERT-base (Devlin et al., 2019), is trained using the RoBERTa pre-training procedure (Liu et al., 2019). Experiments show that BERTweet outperforms strong baselines RoBERTa-base and XLM-R-base (Conneau et al., 2020), producing better performance results than the previous state-of-the-art models on three Tweet NLP tasks: Part-of-speech tagging, Named-entity recognition and text classification. We release BERTweet under the MIT License to facilitate future research and applications on Tweet data. Our BERTweet is available at <https://github.com/VinAIResearch/BERTweet>

language model
english tweets
pre-training procedure
strong baseline
part-of-speech tagging


Generating and Validating Abstracts of Meeting Conversations: a User Study

Abstract: In this paper we present a complete system for automatically generating natural language abstracts of meeting conversations. This system is comprised of components relating to interpretation of the meeting documents according to a meeting ontology, transformation or content selection from that source representation to a summary representation, and generation of new summary text. In a formative user study, we compare this approach to gold-standard human abstracts and extracts to gauge the usefulness of the different summary types for browsing meeting conversations. We find that our automatically generated summaries are ranked significantly higher than human-selected extracts on coherence and usability criteria. More generally, users demonstrate a strong preference for abstract-style summaries over extracts.

abstract
meeting conversation
user study


PubMedQA: A Dataset for Biomedical Research Question Answering

Abstract: We introduce PubMedQA, a novel biomedical question answering (QA) dataset collected from PubMed abstracts. The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts. PubMedQA has 1k expert-annotated, 61.2k unlabeled and 211.3k artificially generated QA instances. Each PubMedQA instance is composed of (1) a question which is either an existing research article title or derived from one, (2) a context which is the corresponding abstract without its conclusion, (3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and (4) a yes/no/maybe answer which summarizes the conclusion. PubMedQA is the first QA dataset where reasoning over biomedical research texts, especially their quantitative contents, is required to answer the questions. Our best performing model, multi-phase fine-tuning of BioBERT with long answer bag-of-word statistics as additional supervision, achieves 68.1% accuracy, compared to single human performance of 78.0% accuracy and majority-baseline of 55.2% accuracy, leaving much room for improvement. PubMedQA is publicly available at <https://pubmedqa.github.io>.


Extracting and evaluating general world knowledge from the Brown Corpus

Abstract: We have been developing techniques for extracting general world knowledge from miscellaneous texts by a process of approximate interpretation and abstraction, focusing initially on the Brown corpus. We apply interpretive rules to clausal patterns and patterns of modification, and concurrently abstract general "possibilistic" propositions from the resulting formulas. Two examples are "A person may believe a proposition", and "Children may live with relatives". Our methods currently yield over 117,000 such propositions (of variable quality) for the Brown corpus (more than 2 per sentence). We report here on our efforts to evaluate these results with a judging scheme aimed at determining how many of these propositions pass muster as "reasonable general claims" about the world in the opinion of human judges. We find that nearly 60% of the extracted propositions are favorably judged according to our scheme by any given judge. The percentage unanimously judged to be reasonable claims by multiple judges is lower, but still sufficiently high to suggest that our techniques may be of some use in tackling the long-standing "knowledge acquisition bottleneck" in AI.

world knowledge
brown corpus
proposition


Event Representation Learning Enhanced with External Commonsense Knowledge

Abstract: Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction. On the other hand, events extracted from raw texts lacks of commonsense knowledge, such as the intents and emotions of the event participants, which are useful for distinguishing event pairs when there are only subtle differences in their surface realizations. To address this issue, this paper proposes to leverage external commonsense knowledge about the intent and sentiment of the event. Experiments on three event-related tasks, i.e., event similarity, script event prediction and stock market prediction, show that our model obtains much better event embeddings for the tasks, achieving 78% improvements on hard similarity task, yielding more precise inferences on subsequent events under given contexts, and better accuracies in predicting the volatilities of the stock market.

external commonsense knowledge
event pair
event representation learning


Learning Word-Class Lattices for Definition and Hypernym Extraction

Abstract: Definition extraction is the task of automatically identifying definitional sentences within texts. The task has proven useful in many research areas including ontology learning, relation extraction and question answering. However, current approaches -- mostly focused on lexicosyntactic patterns -- suffer from both low recall and precision, as definitional sentences occur in highly variable syntactic structures. In this paper, we propose Word-Class Lattices (WCLs), a generalization of word lattices that we use to model textual definitions. Lattices are learned from a dataset of definitions from Wikipedia. Our method is applied to the task of definition and hypernym extraction and compares favorably to other pattern generalization methods proposed in the literature.

definition
hypernym extraction
wikipedia
supervised approach


Discourse Structure and Dialogue Acts in Multiparty Dialogue: the STAC Corpus

Abstract: This paper describes the STAC resource, a corpus of multi-party chats annotated for discourse structure in the style of SDRT (Asher and Lascarides, 2003; Lascarides and Asher, 2009). The main goal of the STAC project is to study the discourse structure of multi-party dialogues in order to understand the linguistic strategies adopted by interlocutors to achieve their conversational goals, especially when these goals are opposed. The STAC corpus is not only a rich source of data on strategic conversation, but also the first corpus that we are aware of that provides full discourse structures for multi-party dialogues. It has other remarkable features that make it an interesting resource for other topics: interleaved threads, creative language, and interactions between linguistic and extra-linguistic contexts.

multiparty dialogue
stac corpus
discourse structure


Committed Belief Annotation and Tagging

Abstract: We present a preliminary pilot study of belief annotation and automatic tagging. Our objective is to explore semantic meaning beyond surface propositions. We aim to model people's cognitive states, namely their beliefs as expressed through linguistic means. We model the strength of their beliefs and their (the human) degree of commitment to their utterance. We explore only the perspective of the author of a text. We classify predicates into one of three possibilities: committed belief, non committed belief, or not applicable. We proceed to manually annotate data to that end, then we build a supervised framework to test the feasibility of automatically predicting these belief states. Even though the data is relatively small, we show that automatic prediction of a belief class is a feasible task. Using syntactic features, we are able to obtain significant improvements over a simple baseline of 23% F-measure absolute points. The best performing automatic tagging condition is where we use POS tag, word type feature AlphaNumeric, and shallow syntactic chunk information CHUNK. Our best overall performance is 53.97% F-measure.


Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

Abstract: Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society. This phenomenon is pervasive and consistent across different word embedding models, causing serious concern. Several recent works tackle this problem, and propose methods for significantly reducing this gender bias in word embeddings, demonstrating convincing results. However, we argue that this removal is superficial. While the bias is indeed substantially reduced according to the provided bias definition, the actual effect is mostly hiding the bias, not removing it. The gender bias information is still reflected in the distances between “gender-neutralized” words in the debiased embeddings, and can be recovered from them. We present a series of experiments to support this claim, for two debiasing methods. We conclude that existing bias removal techniques are insufficient, and should not be trusted for providing gender-neutral modeling.

debiasing method
word embedding
gender bias
bias removal technique
implicit bias


Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change

Abstract: State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical semantic change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical semantic change and systematically control for possible biases.

temporal referencing
lexical semantic change
change detection


MoEL: Mixture of Empathetic Listeners

Abstract: Previous research on empathetic dialogue systems has mostly focused on generating responses given certain emotions. However, being empathetic not only requires the ability of generating emotional responses, but more importantly, requires the understanding of user emotions and replying appropriately. In this paper, we propose a novel end-to-end approach for modeling empathy in dialogue systems: Mixture of Empathetic Listeners (MoEL). Our model first captures the user emotions and outputs an emotion distribution. Based on this, MoEL will softly combine the output states of the appropriate Listener(s), which are each optimized to react to certain emotions, and generate an empathetic response. Human evaluations on EMPATHETIC-DIALOGUES dataset confirm that MoEL outperforms multitask training baseline in terms of empathy, relevance, and fluency. Furthermore, the case study on generated responses of different Listeners shows high interpretability of our model.


Learning to Translate in Real-time with Neural Machine Translation

Abstract: Translating in real-time, a.k.a.simultaneous translation, outputs translation words before the input sentence ends, which is a challenging problem for conventional machine translation methods. We propose a neural machine translation (NMT) framework for simultaneous translation in which an agent learns to make decisions on when to translate from the interaction with a pre-trained NMT environment. To trade off quality and delay, we extensively explore various targets for delay and design a method for beam-search applicable in the simultaneous MT setting. Experiments against state-of-the-art baselines on two language pairs demonstrate the efficacy of the proposed framework both quantitatively and qualitatively.

real-time
neural machine translation
read
latency
source sentence


SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets

Abstract: In this paper, we present the results of the SemEval-2020 Task 9 on Sentiment Analysis of Code-Mixed Tweets (SentiMix 2020). We also release and describe our Hinglish (Hindi-English)and Spanglish (Spanish-English) corpora annotated with word-level language identification and sentence-level sentiment labels. These corpora are comprised of 20K and 19K examples, respectively. The sentiment labels are - Positive, Negative, and Neutral. SentiMix attracted 89 submissions in total including 61 teams that participated in the Hinglish contest and 28 submitted systems to the Spanglish competition. The best performance achieved was 75.0% F1 score for Hinglish and 80.6% F1 for Spanglish. We observe that BERT-like models and ensemble methods are the most common and successful approaches among the participants.

sentiment analysis
code-mixed tweets
semeval-2020 task
code-mixed text
twitter


Quantifying Word Order Freedom in Dependency Corpora

Abstract: Using recently available dependency corpora, we present novel measures of a key quantitative property of language, word order freedom: the extent to which word order in a sentence is free to vary while conveying the same meaning. We discuss two topics. First, we discuss linguistic and statistical issues associated with our measures and with the annotation styles of available corpora. We find that we can measure reliable upper bounds on word order freedom in head direction and the ordering of certain sisters, but that more general measures of word order freedom are not currently feasible. Second, we present results of our measures in 34 languages and demonstrate a correlation between quantitative word order freedom of subjects and objects and the presence of nominative-accusative case marking. To our knowledge this is the first large-scale quantitative test of the hypothesis that languages with more word order freedom have more case marking (Sapir, 1921; Kiparsky, 1997).

word order freedom
dependency corpora
presence


Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions

Abstract: In this paper, we propose to study the problem of court view generation from the fact description in a criminal case. The task aims to improve the interpretability of charge prediction systems and help automatic legal document generation. We formulate this task as a text-to-text natural language generation (NLG) problem. Sequence-to-sequence model has achieved cutting-edge performances in many NLG tasks. However, due to the non-distinctions of fact descriptions, it is hard for Seq2Seq model to generate charge-discriminative court views. In this work, we explore charge labels to tackle this issue. We propose a label-conditioned Seq2Seq model with attention for this problem, to decode court views conditioned on encoded charge labels. Experimental results show the effectiveness of our method.

charge prediction
criminal case
fact description


Effects of Game on User Engagement with Spoken Dialogue System

Abstract: In this study, we examine the effects of using a game for encouraging the use of a spoken dialogue system. As a case study, we developed a word-chain game, called Shiritori in Japanese, and released the game as a module in a Japanese Android/iOS app, Onsei-Assist, which is a Siri-like personal assistant based on a spoken dialogue technology. We analyzed the log after the release and confirmed that the game can increase the number of user utterances. Furthermore, we discovered a positive side effect, in which users who have played the game tend to begin using non-game modules. This suggests that just adding a game module to the system can improve user engagement with an assistant agent.

game
user engagement
spoken dialogue system


A Report on the 2017 Native Language Identification Shared Task

Abstract: Native Language Identification (NLI) is the task of automatically identifying the native language (L1) of an individual based on their language production in a learned language. It is typically framed as a classification task where the set of L1s is known a priori. Two previous shared tasks on NLI have been organized where the aim was to identify the L1 of learners of English based on essays (2013) and spoken responses (2016) they provided during a standardized assessment of academic English proficiency. The 2017 shared task combines the inputs from the two prior tasks for the first time. There are three tracks: NLI on the essay only, NLI on the spoken response only (based on a transcription of the response and i-vector acoustic features), and NLI using both responses. We believe this makes for a more interesting shared task while building on the methods and results from the previous two shared tasks. In this paper, we report the results of the shared task. A total of 19 teams competed across the three different sub-tasks. The fusion track showed that combining the written and spoken responses provides a large boost in prediction accuracy. Multiple classifier systems (e.g. ensembles and meta-classifiers) were the most effective in all tasks, with most based on traditional classifiers (e.g. SVMs) with lexical/syntactic features.

native language identification
nli
learned language