In the complete ABSA task, the aspect terms are unknown and SC will assign a polarity to every word.

aBSa normally involves three subtasks, namely aspect term extraction (aE), opinion term extraction (OE), and aspect-level sentiment classification (SC).


Given a clear recipe to produce the values in the selected figures and tables, we assume that the aim is closer to replication according to our definition.

this paper presents our reproduction efforts of an earlier study of automatic essay scoring (AES) for determining the proficiency of second language learners in a multilingual setting.


We find that the model trained on backtranslated data is often conservative choosing to leave many input sentences almost unchanged.

our final sentence paraphrasing dataset used to train the abstractor P PM contains 8.6M pairs.


We find that even when some context window performs well on the source language task, that is often not the best choice for the target language.

the performance of the small-window embeddings does not transfer to the test languages.


Based on the attentional encoder-decoder framework (Bahdanau et al., 2015), it optimizes model parameters under direct translation supervision.

to cascading, another option is to perform direct speech-to-text translation.


The results on the TREC dataset are very close because top 5 words are possible to include all informative words for short sentences with the average length of 10.

we adopt SP-LIME proposed by Ribeiro et al.


We evaluate the local interpretability of VMASKbased models against the base models via the AOPC score (Nguyen, 2018;Samek et al., 2016) and the global interpretability against the IBAbased models via post-hoc accuracy (Chen et al., 2018).

in each iteration, the first term in Equation 9 is approximated with a single sample from q(R|x (i) ) (Kingma and Welling, 2014).


As shown in our motivating example in Figure 1 and the constructed graph in Figure 3, the reasoning process needs to operate on span/argument-level, where the basic computational unit typically consists of multiple words like "Rodney King riots" and "the most popular county in the USA".

the algorithm begins from nodes without incident relations.


While this makes sense for most existing datasets, it does not take into account the highlighted cells t highlight in our task.

each example in the development and test sets was annotated by three annotators.


The original BERT model is pre-trained with both Wikipedia and the Books corpus (Zhu et al., 2015), the former of which contains our (unrevised) test targets.

the collected annotation examples are noisy since a sentence s may only be partially supported by the table t.


In this work, we propose TOTTO, an opendomain table-to-text generation dataset that introduces a novel task design and annotation process to address the above challenges.

data-to-text tasks that are limited to verbalizing a fully specified meaning representation (Gardent et al., 2017b) do not test a model's ability to perform inference and thus remove a considerable amount of challenge from the task.


We apply a separately trained language model to calculate the likelihood of the given sentence, which is similar to previous works (Miao et al., 2019; Liu et al., 2019).

we run our algorithm for 100 epochs, and finally, select the sentence with the smallest loss as the final result.


In this section, we would like to introduce the design of differentiable cost functions for different objectives.

when the search space is too large, beam-search-based methods always fail to find the constrained optimal solution.


This method makes use of parallel corpora as well as word alignments to learn cross-lingual embeddings.

note that we report this to situate our results in context of the same, and these cannot be directly compared, since each task's SOTA is obtained by varied training architecture, suited to perform well in one particular task alone.


Zero pronoun resolution (Zhao and Ng, 2007; Kong and Zhou, 2010; Chen and Ng, 2016; Yin et al., 2017, 2018) is a line of research closely related to dropped pronoun recovery.

in dropped pronoun recovery, we consider both anaphoric and non-anaphoric pronouns, and attempt to recover the type of dropped pronoun but not its referent.


We randomly split the 87-word lowest-IAA class (15) into three equal 29-word subsets.

only some of those pairs are included in SimVerb (e.g., out of Class 9 pairs decidechoose, decide-select, decide-elect, decide-pick, only the first one is present in SimVerb).


This is followed by another set of max-pooling and average-pooling layers identical to the first layer.

the objective behind creating two gold-standards is to enable the researchers to evaluate the hatespeech detection approaches on both easier (coarsegrained) and challenging (fine-grained) scenarios.


These results substantiate the findings on coarse-grained classification task which suggest that instead of training embeddings, using existing pre-trained embeddings by fine-tuning them on the task in hand is a more perceptive choice.

rest of the paper is organized as follows.


As RomUrEm is already trained on a corpora of hate-speech tweets, the same cannot be concluded for this particular embedding.

Tweet: Aap apni behan. Beti.. maan ... aur bivi ka march karwa do phir Translation: Then do a march of you sister, daughter, mom and wife


This is closely followed by two other ensemble models i.e., BERT+LASER+GBDT and BERT+LAMB, with an F1-score of 0.89.

on forwarded high activations, the notion of bigram, trigram, and quadgram holds true.


Additional experiments related to efficiency of intent classification have been conducted by Casanueva et al. (2020).

we also probe the usefulness of ConveRT encodings for transfer learning in the intent classification task: the model must classify the user's utterance into one of several predefined classes, that is, intents (e.g., within e-banking intents can be card lost or replace card).


As for correction performance, Table 6 shows the evaluation result of our human evaluation.

(...) It features the spirited siblings from the beloved classic "One Fish Two Fish Red Fish Blue Fish" and is believed to have been written between 1958 and 1962.


BART is a sequence-to-sequence auto-regressive transformer model that is pretrained as a denoising auto-encoder.

low recall on the inconsistent summaries and false positive samples remain as challenges.


Inspired by ULMFiT (Howard and Ruder, 2018), we propose a two-stage pre-training method to train our lattice language model.

applying these models to the spoken scenarios poses several discrepancies between the pre-training task and the target task, such as the domain mismatch between written texts and spoken utterances with ASR errors.


QE datasets should have a mixture of instances that model both high and low adequacy irrespective of the fluency.

when we examine the estimations from our QE models, we find that they rarely output values above 0.3, which implies that these models fail to capture sentences with lowquality scores.


Our models perform competitively to the state-of-theart QE models (Kepler et al., 2019a;Kim et al., 2019).

With the advent of NMT models, we have seen an increase in the quality of translation systems.


The strong performances on partial-inputs show that these datasets are cheatable, and QE systems trained on them would not generalize well (Feng et al., 2019).

hTER would miss-classify it as a good translation since there is only one token that requires post-editing.


ALIGN outperforms SEQ2SEQ + in ACC EXE on 9 out of the 10 most frequent templates.

table H5: Dev logical form (ACC LF ), template (ACC tEMP ) and column (ACC COL ) accuracies on the 10 most frequent templates.


Consider the bottom example in Figure 1: A decoder can benefit from knowing that ORDER BY . . . LIMIT 1 comes from “the highest” (where rank 1 is best); and an encoder should

wTQ questions are authored by humans under no specific constraints, and as a result wTQ includes more diverse semantics and logical operations.


Comparatively, our method enforces faithfulness by including the proposed table-text optimal-transport matching loss and table-text embedding similarity loss.

we train our models end-to-end to minimize our objective function with/without the copy mechanism.


We design our evaluation criteria based on (Wang et al., 2018; Tian et al., 2019), but our criteria differs from (Tian et al., 2019) in several aspects.

1: The sentence is not fluent at all and people cannot understand its meaning.


✗ means do not use the corresponding column component.

one key element of our model is to enforce a generated text sequence to be consistent with (or faithful to) the table input.


Note that the precision in PARENT-T corresponds to the percentage of words in a text sequence that co-occur in the table; and the recall corresponds to the percentage of words in a table that co-occur in the text.

let Pvocab be the output of the Transformer decoder.


This special issue includes three papers that focus on different crosslingual challenges at the level of the lexical representation.

shorter sentence representations increase the accuracy in non-trainable similarity tasks.


Reversed and symmetric question prefixes are converted to a more general label for fair comparison.

we repeat this process 3 times and show the average agreement scores in Table 7.


Discourse relations describe how two propositions relate to one another, and identifying them automatically is an integral part of natural language understanding.

we implement the model using BERT (Devlin et al., 2019) in its standard fine-tuning setting, except that the Softmax layer is replaced by a Sigmoid activation function to support multi-label classification.


The QG models are based on BERT-HLSQG (Chan and Fan, 2019), with the hyperparameters provided by the authors and minor modifications to apply penalized sampling as in Keskar et al. (2019).

the second example suggests that our model suffers from the problem of handling pronouns.


The model mainly plays the role of a leading player assigned with an explicit goal, a knowledge path comprised of two topics, and is provided with knowledge related to these two topics.

based on the Seq2Seq framework, we aggrandize it with each key component of model KIC progressively and the results are summarized in Table 4 and Table 5.


Namely, for T1, we invert the order of tokens between two entities of REVERSE instances and treated the resulting token sequences as non-REVERSE instances.

in our reproduction work, at each run we set the random state by selecting the number of the random seed from a range of 0 to 10000 to ensure the train and validation data is split without being introduced too much human bias.


We experimented with various cutoff dates in an attempt to replicate the data that would have been available at the time of publication of the ETH-DS3Lab system, and picked December 31, 2017 (the deadline of the SE18T7 was January 22).

with the advancement of Information Retrieval (IR) techniques and Human Language Technology (HLT), text-based search engines have played a major role in research, communication, and publication in many academic fields.


In this section, we present the experiments on eventevent relation extraction.

despite the scarce annotation for both tasks, the proposed method surpasses the SOTA TempRel extraction method on MATRES by relatively 3.27% in F 1 ; it also offers promising performance on the HiEve dataset for subevent relation extraction, relatively surpassing previous methods by at least 3.12% in F 1 .


The TempRels are considered for all event pairs on the same axis and within a context of two adjacent sentences.

the proposed joint constrained learning framework surpasses the best baseline method by a relative gain of 3.27% in F 1 , and excels in terms of both precision and recall.


This is motivated by the idea that while the average number of “old” citations per paper is stable (cf. Sec. 3.2), they might be distributed in an unbalanced way.

future work includes a deeper qualitative analysis of which (type of) papers are being cited; a more fine-grained analysis of different research topics in NLP to determine whether changes are more prevalent within certain areas than others; or extending the analysis to a larger set of the papers in the ACL Anthology.


We use the train, validation, test split provided by Hancock et al. (2018) for Disease, and split the development set of Spouse randomly into a validation and test set (the split was done at a document level).

we trained ExpBERT and the NoExp baseline with varying fractions of Spouse and TACRED training data (Figure 3).


The incorporation of text into ideal point modeling is not limited to legislators: Sim et al. (2016) model U.S. Supreme Court behavior using a generative model for amicus briefs

we present an embedding-based model for predicting the frequency and sentiment of legislator tweets.


These augmented sentences generated from Insertion are often not natural, since those inserted mgrams are randomly picked from the whole corpus which may be irrelevant to the current sentence.

their results could not serve as augmented data to pretrain sequence tagging models, since there was no indication where were the disfluent segments in output sentences.


This example shows that syntactic structure provides useful cues for CWS and POS tagging.

we use existing language processing toolkits to obtain the auto-analyzed syntactic knowledge for the context, and the proposed attention module can learn and benefit from them although their quality may not be perfect.


We experimented on two datasets from SemEval 2014 (Pontiki et al., 2014), which consist of reviews and comments from two categories: LAPTOP and RESTAURANT.

giving the superior performance of BERT on downstream tasks, it is natural to ask if BERT is learning an empirically useful structure of language.


UD is based on a lexicalist view of syntax, which means that dependency relations hold between words, and that morphological features are encoded as properties of words with no attempt at segmenting words into morphemes.

Conjoined predicates often share dependents (e.g., a subject) and conjoined dependents share a head.


It is key for models to not ignore identifiers, but to match them with the right context.

we use post-hoc explanation algorithms to interpret and modulate finetuned language models like BERT (Devlin et al., 2018), which achieve state of the art performance on many hate speech detection tasks (MacAvaney et al., 2019;Mandl et al., 2019).


We would therefore not have been able to provide a url to this data as is required for submissions to the REPROLANG 2020 shared task.

These thirteen language sets are Danish, Spanish, Basque, French, French Sequoia, Hungarian, Croatian, Indonesian, Japanese, Dutch Lassy Small, Norwegian Bokmaal, Norwegian Nynorsk, and Russian SynTagRus, which was noted by Bohnet et al. (2018) in the caption of Table 2 of their document.


Our comparison of two model configurations shows the parameter values from the paper resulting in F1-scores that are closest to the reported scores for 25 out of 46 data sets, with 1 tie.

some of the languages in the data do not have meaningful XPOs tags.


Our models use the acronyms WWM and SWM respectively to indicate the type of masking they used.

first important result that should be noted is that LSTM+CRf and LSTM+seq2seq models have similar performances to that of the SEM (CRf) baseline when they are not augmented with any kind of embeddings.


Just adding classical fastText word embeddings dramatically increases the performance of the model.

as opposed to adding ELMo, the difference with/without CamemBERT is equally considerable for both the LSTM-seq2seq and LSTM-CRF.


The LDC2015E86 dataset contains 16,833 instances for the training, 1,368 for the development, and 1,371 for the test.

the overall architecture of Graph transformer is shown in Figure 2, with an example AMR graph and its corresponding sentence.


Such a method may lose information from the graph structure.

these graph encoders still cannot significantly outperform sequence encoders.


Both of its encoder and decoder consist of 6-layer Transformers, where the inner dimension of feed-forward networks (FFN), hidden state size, and the number of attention head are set to 4096, 1024, and 16.

we implement Back-Translation based on the source code6 to generate a new question data for each original question; (3) Text-VAE (Bowman et al., 2016;Liu et al., 2019a): it uses RNNbased VAE to generate a new question data for each question of SQuAD 2.0.


Different from previous work, ZEN provides an alternative way of learning larger granular text for pre-trained models, where the structure of BERT is extended by another Transformer-style encoder to represent the extracted n-grams for each input text instance.

The second step of n-gram extraction is performed during pre-training, where some n-grams in L are selected according to each training instance c = (c1, c2, ..., ci, ..., ckc) with kc characters.


It shows that the number 32 (2 5 ) gives a good tradeoff between performance and computation, although there is a small gain by using more n-grams.

first, both methods rely on the word masking strategy so that the encoder can only be trained with existing word and phrase information.


If we consider the Direct CLWE approach in terms of Levenshtein distance, the distance between a word in the corpus, and a word in the lexicon, must be 0 for them to match.

Table 7: Keystroke saving rate for language models trained for both tokenization strategies, with differing approaches to initialization, for varying numbers of suggestions, on both the dev and test sets.


This is expected because during inference, many key terms may be masked out, making the task harder.

in both examples, the discourse marker appears in the first sentence but the second sentence has anaphora referring to an antecedent in the first sentence.


We show that our model achieves a new stateof-the-art on DiscoEval, improving the results on 5 of the 7 tasks and increasing accuracy by up to 13% and an average of over 4% absolute across all tasks.

we are unable to achieve similar results despite training on 24 times more examples, and including CCNews (Liu et al., 2019), a larger and higher quality data source.


Two economic advisers estimated in a 2009 report that with the stimulus plan, the unemployment rate would peak near 8 percent before dropping to less than 6 percent by now.

table 8 presents examples of selected oracles that serve as gold labels during training the extractive summarization model.


Table 8 presents examples of selected oracles that serve as gold labels during training the extractive summarization model.

explain-extr is ranked higher than explain-MT in terms of Non-redundancy and Non-contradiction, where the last criterion was disagreed upon, and the rank improvement for the first one is only marginal at 0.04.


In this example, the model is penalized for Claude, even though the time-interval for Claude exactly matches the query.

e.g., Pierre is also a member of the assembly, but during [2002,2003].


Each interval T is represented as [t b , t e ], with begin and end time instants.

all the datasets we used had at least 99% of facts with temporal information.


Natural Language Visual Reasoning 2 (NLVR2) Given a natural language sentence about two photographs, the task is to determine if the sentence is true .

contrast sets are model-agnostic, constructed by experts to characterize whether a model's decision boundary locally aligns to the true decision boundary around some point.


The masking proportion is lifted from 15% to 20% without tuning compared with BERT and RoBERTa.

gLM yields an extra 1.3% (67.7% to 69.0%) improvement by better data exploitation.


It was therefore not entirely surprising to us that some colleagues specializing on the subject participated in the game.

For each test, we purposely crafted two short sentences, with only one possible annotation, so that the players can easily apply their new knowledge, gain confidence, and not be confused upon completing the training phase.


Each entry can have zero, one or multiple translations.

we repeat this process for each of the three paired lists and merge the resulting lists, removing duplicate entries in the process.


Finally, we compare the results for both the monolingual and multilingual models to understand how they  perform on the French dataset.

finally we extend our thanks to the whole Illuin Technology team for their reviewing and constructive feedbacks.


The more answers to a question there are, the more likely it is that any other answer is equal to one of the expected answers.

we observe also that enriching native French training data with the translated samples does not improve the performances on the native evaluation set.


v,v denote the congruent image and the incongruent image, respectively.

However, our experimental results show that the model of Xu et al. (2019) may be insufficient to identify which attribute a value corresponds to.


Though plenty of systems have been proposed to supplement product attribute values (Putthividhya and Hu, 2011;  More, 2016; Shinzato and Sekine, 2013; Zheng et al., 2018; Xu et al., 2019), the relationship between product attributes and values are not sufficiently explored, and most of these approaches primarily focus on the text information.

to sum up, using the visual product information indiscriminately poses detrimental effects on the model, and selectively utilizing visual product information with global and regional visual gates are essential for our tasks.


For example, given a sentence "The red collar and golden buttons in the shirt form a colorful fashion topic" and the predicted product attribute "Color", it is easy to recognize the value "golden" corresponding to attribute "Color" instead of "Material".

we evaluate the ScalingUp model to predict the value for each given attribute on our dataset, and the result is unsatisfactory.


We use the original baseline system and interchange transformer weights to compare between BERT and ours.

2019-19051600006 under the BETTER Program and by Contract FA8750-19-2-1004 with the US Defense Advanced Research Projects Agency (DARPA).


As described in detail in Section 2, past reevaluations of human parity claims were hampered by sub-optimal test settings.

they focus only on the effect of translationese with re-spect to human evaluation, without considering its differing effect on automatic evaluation.


Cross-entropy loss based optimization is traditionally used for the sequence generation tasks.

bandit-based variant to improve all metrics but by using multiple bandits which are controlled by a controller, called Hierarchical Multi-reward Bandits (HM-Bandit, Fig. 2).


Very dissimilar usage distributions yield high JSD whereas low JSD values indicate that the proportions of usage types barely change across periods.

we propose a novel method that exploits the BERT neural language model to obtain representations of word usages, clusters these representations into usage types, and measures change along time with three proposed metrics.


For that reason we generated artificial data automatically labeled with slot and intent labels, defined by a smart home context.

table 7 (Seq2seq(6)) includes performances significantly lower than the other Att-RNN and Seq2seq models due to a strong tendency towards none intent prediction with ESLO2 data as part of the training set (Figure 3 Seq2seq(6)) (Desot et al., 2019a;Desot et al., 2019b).


Only out of domain utterances were kept for collecting none intent training data.

an artificial data version was generated without alignment between source and target series of labels and intent classes for a sequence generation approach.


Table 7 (Seq2seq(6)) includes performances significantly lower than the other Att-RNN and Seq2seq models due to a strong tendency towards none intent prediction with ESLO2 data as part of the training set (Figure 3 Seq2seq(6)) (Desot et al., 2019a;Desot et al., 2019b).

these systems include multiple modules, such as a Spoken Language Understanding (SLU) module that must be able to extract the intent of the user from the voice command and its named entities.


Percentages of words with more than 3 morphemes (>3 Morph.), surface segmentation (Surf.), canonical segmentation (Canon.), and without segmentation (NoSeg.), as well as the average number of morphemes per word (M./W.) and characters per word (Ch./W.).

by manual inspection, we identify five not mutually exclusive types of errors: Oversegmentation (Overseg.) arises when the number of morpheme boundaries in the prediction is higher than in the gold standard annotation.


PGNet shows no strong wins or problems regarding these errors, except for English, where it performs better for undersegmentation.

instead of using maximum likelihood estimation (MLE), training is done with imitation learning.


The performance of IL is consistently better on all metrics, with substantial gains for Tepehua of 6.5% accuracy over the closest system (semiCRF) and 10.4% accuracy over PGNet.

we find a large gap between the emulated and the real low-resource scenarios: while accuracy is above 50% for all high-resource languages even with reduced amounts of training data, for Popoluca and Tepehua, our best model only obtains 37.4% and 28.4% accuracy, respectively.


Previous methods have mostly relied on supervised learning techniques which require labeled corpora.

it efficiently captures the skew in the distribution of languages, thereby making the disparity in language usage more clearer.


These Process entities can be further selected by combining another query, for example, a compound name.

these Process entities can be further selected by combining another query, for example, a compound name.


To evaluate the performance of cyberbullying detection methods, we use the following metrics, which are commonly used to evaluate classifiers: Accuracy (Acc), Precision (Pre), Recall (Rec), and F1-Score (F1).

the latter comment is selected to be a more important evidence for cyberbullying prediction.


To measure the difference, we calculate (1) KL divergence between two distributions (positive/negative) and (2) intersection over union (IOU), which is a widely used metric in object detection (Ren et al., 2015) on the top-1 memory usage.

each memory slot in Lample et al. (2019) and ours does not have explicit meaning.


For BERT, words in the Verb set were removed if they were assigned more than one token, as BERT does not model the joint distribution over multiple masked tokens.

this variation is not explained by frequency of occurrence in natural text.


In order to assess the quality of OCR for AMZ, 23 we transcribed a small random sample of pages of issue 25.

12 Part of this material is also freely downloadable from The Language Bank of Finland provided by the FIN-CLARIN consortium.


In this case, the estimate Q(a, s) can be arbitrarily bad (Fujimoto et al., 2018).

we solve these problems by developing a new method for offline RL.


While our KL-control models achieved higher qualitative ratings than the other offline RL algorithms, none of the RL models received higher qualitative ratings than the VHRED-EI Model (Table 4).

we reward semantic similarity between the user's input and the bot's response, to encourage the bot to stay on topic and produce reasonable answers.


We conduct experiments on synthetic, hand-authored, and human-paraphrased rule-bases to show promising results for QA and proof generation, with strong generalization performance.

model Interpretability: PROVER follows a significant body of previous work on developing interpretable neural models for NLP tasks to foster explainability.


This helps us understand the effectiveness of our NAF learning; (3) Unconstrained Train + No ILP: Through this model, we study the effectiveness of our global constraints.

multi-hop QA datasets like HotpotQA (Yang et al., 2018) require multiple reasoning steps, but the inference rules needed are again implicitly inferred, rather than explicitly provided.


We evaluate the generalization ability of PROVER compared to RuleTakers by training models on the train splits of DU0, DU1, DU2 and DU3, and testing the QA performance on the overall test set for DU5, which includes questions with higher depth than seen during training.

their task requires predicting the truth value of a statement by reasoning over a set of facts and rules, all expressed in natural language.


Although the bulk semantic information is based on adjectives in this kind of sentences, the copular verb "to be" does play a role in the sentence and annotation of the copular verb is also required for complete semantic representation.

the verb has to have a base form that can stand on its own and the base has to share its definition with the derived form.


And finally, sample sentences were added for each entry in the data set.

the annotations include not only arguments but adjuncts as well.


Verbs constitute a major category in human languages, expressing the critical information concerning a state or an event.

verbs can be categorized based on the number of their core arguments: intransitive (1), transitive (2) and ditransitive (3).


One example is -(y)Abil, historically derived from the verb bil-"to know", which adds the meaning of ability, permission, or possibility.


Note that many existing generator architectures (Miyato and Koyama, 2018; Wang et al., 2018; Karras et al., 2019, 2020) have residual blocks starting from higher dimensions (eg. 512, 1024) in lowresolution then gradually decrease the dimension as feature maps are spatial upsampled.

we use the same vocabulary used in BERT 4 and LXMERT with size 30522.


The AVG model performs slightly better than the BiLSTM on both generation tasks.

for each object, we collect its id, type (boundary, bar, jar, circle), color (red, green, blue, purple, gray, black), state (dynamic, static), and (x, y) coordinates.


To learn c's representation, we encode both word occurrence in each turn (via turn-level modeling) and interactions between conversation turns (via conversation-level modeling).

our model performs the best in conversation cold start.


This phenomenon is referred to as conversation cold start.

graph-structured encoder may be a suitable alternative for capturing rich turn interactions in Reddit conversations.


Rather than blaming individual shooters, the German press paid more attention to U.S. public opinion manifesting as gun violence protests and the U.S. gun regulations.

their approach predicts only a single frame, which is insufficient given the multifaceted nature of news framing in which multiple frames often co-occur in the same headline.


Each of these matrices is of size vocab_size � hidden_size, and thus each token in the vocabulary has its corresponding q, k, and v vectors.

we replace all utterances of "important" words with it's T L dictionary translation.


These experiments used 100 examples sampled from the manually annotated CNN/DM test set.

on the more abstractive XSum data, results show a reverse trend with the MNLI model achieving highest performance.


When training our CatBoost model (section 2.2.) we did not have a manually-annotated evaluation set available to tune against

for example, letras giz vetores (Spanish) was predicted to be Portuguese and cacau seco (Portuguese) as Spanish.


It is a powerful fundamental model for wide use of natural language processing tasks.

with a few vetting-estimating iterations, evaluation results can be dramatically close to that of human evaluation by using limited vetted data and all noisy data.


PCNN+ATT+RL (Qin et al., 2018b) adopts reinforcement learning to overcome wrong labeling problem for distant supervision.

for the PR curve, every point depends on P @K for different K. this vetting strategy is also useful for the PR curve.


Our model achieves new state-of-the-art results of 70.49% (German) and 90.75% (Czech) F1-score, significantly outperforming the previous best system (Lyu et al., 2019).

we use mean field variational inference approximates to obtain the final arc distribution.


In order to generate story-like textual descriptions that complement the factual captions, we additionally train our model to exploit our diverse completesentence annotations.

this setting is consistent with the NSP task in (Devlin et al., 2019).


We use SpaCy linguistic features (Honnibal and Montani, 2017) along with the LemmInflect library (https://github.com/bjascob/LemmInflect) and templatebased generation to convert the captions, intentions, effects, and attributes from V2C to create questions and ground-truth answers.

we want to make sure that these descriptions are also relevant to the video.


The word2vec word embeddings for the Polish language were downloaded from Wikipedia2vec (Yamada et al., 2018) and cleaned by removing embeddings of tokens consisting of multiple words.

as mentioned earlier, the self-learning procedure consists of two steps.


It is clear that such a strong assumption does not hold exactly since that would mean that e.g., the languages have the same number of words or that they have the same number of synonyms for each concept.

we show that some Slavic languages (Czech and Polish) are quite challenging for the method.


A major issue is that engineers know little biology or few details of plants or animals.

determining whether the sentence expresses a trade-off requires additional reasoning and often domainspecific knowledge.


The training set contains 30% more images with "woman cooking" than "man cooking".

we further propose a bias mitigation approach based on posterior regularization.


We posit that the requirement of making hard predictions (i.e., maximum a posteriori estimation) also amplifies the bias when evaluating the top predictions.

given corpus-level constraints and a distribution predicted by a model, we 1) define a feasible set of the distributions with respect to the constraints; 2) find the closest distribution in the feasible set from given distribution; 3) do maximum a posteriori (MAP) inference on the optimal feasible distribution.


Though our experiments are based on a Chinese dataset, our approach can be easily adapted to other languages, such as English and Japanese.

figure 2: The historical dialogue selection module.


proposed a review response generation model in the E-commerce platform, which used the reinforcement learning and copy mechanism to fuse external product information, thereby generating informative and diverse responses.

these models are difficult to generalize, and their results are unsatisfied since responses maybe vary a lot for the same question towards different occasions and speakers.


The application of a curriculum is based on the often unspoken assumption that the representation of a complex pattern can be reached more easily from a simpler pattern.

this could be explained by the idea that they never acquire the either/or rule (instead memorizing the entire sequence).


For example, a "slice of cake" can be broken into the individual meanings of "slice", "of", and "cake", but an idiomatic expression such as "piece of cake", meaning a simple task, cannot be broken into the individual meanings of "piece", "of", and "cake".

knowing that "trick" and "student" co-occur is not sufficient to clarify the meaning and connotations of either word or compose a shared meaning.


Each of the baseline model is a binary classifier indicating whether a given phenotype is present in the input patient note.

example: "past medical history of alcohol abuse who was diagnosed with alcoholic cirrhosis on this admission".


To the best of our knowledge, this is the first paper that attempts solving dialogue evaluation by treating it as an anomaly detection problem.

participants had to create dialogue systems that had to fulfill specific criteria.


In addition, Da and Kusai (2019) proves that pre-trained language models have the ability to encode some commonsense knowledge in the embedding space through the attribute classification evaluation.

from figure 5, we can see that the layout of question, the correct answer 'garage' and the wrong answer 'utility room' are 'a person' with 'a truck', 'cars', and 'chairs' and 'old televisions', respectively.


For example, we could find many images where fire extinguishers appear in these scenes of public places.

here we use commonsenseQA as an example to demonstrate our method.


Experimental results on two commonsense reasoning problems, i.e. commonsense question answering and pronoun resolution, demonstrate that Loire outperforms traditional language-based methods.

they also show that the encoded commonsense knowledge is limited, which could be improved by introducing some supplementary data, like ConceptNet.


Detailed evaluation metrics for this process are described in Dumitrache et al. (2018).

it was translated into the word meog-da 'eat' because the word aziwau was used as a metaphor in the Japanese sentence to represent the concept of ingestion.


When the results were evaluated, no significant difference was observed between the two groups of workers and the annotation quality.

the crowd workers tended to select more specific frames instead of comprehensive frames, such as the frame Intentionally act.


In (Akhtar et al., 2016), a dataset for Hindi was created and in (Akhtar et al., 2018), an approach was developed for ABSA in Hindi using the created dataset.

the number of neutral aspect terms is relatively low.


For instance, the marker +Aux differentiates auxiliary from other verbs, the marker +NProp differentiates proper nouns from other nouns, while markers +ProN and +ProA differentiate nominal from adjectival pronouns.

this comes as no surprise, due to the fact that it is a very specific text, which is fully covered by the new dictionary used for the tt19 model.


The developers reveal that this is due to the underlying architecture that contains a hidden convolutional layer shared between a tagger, parser and a named entity recognizer.

publishing of new versions of annotated Serbian corpus requires an accurate automatic tagging system.


Raw data are stored in a specific folder (raw/), as are additional information pertaining to reference catalogs, such as Glottocodes or orthography profiles (etc/), or additional, userprovided commands (commands/).

with cldfbench, we hope to make a first step into the direction of potential users who might want to try to lift their own or other published datasets to a higher level of cross-linguistic comparability.


Technically, cldfbench provides a layer on top of the pycldf package that provides low-level access to read, write, and validate CLDF datasets (https://pypi.org/pycldf).

cldfbench encourages transparent, replicable data curation -which is essential for making constantly evolving data Reusable.


An example of this task is provided in Figure 1.

For each unlabeled dataset, we create a randomly sampled validation set of about 30K samples during these experiments.


The storage overhead is minor compared to non-structured irregular pruning (Han et al., 2016).

we report our reproduced results using the same network architectures in the second row.


In particular, we consider three measures based on n-gram matching: BLEU-2 (Papineni et al., 2002), ROUGE (Lin, 2004), and observed.

this is in line with Downing (1977), who argues that novel (i.e., not yet lexicalised) noun-noun compounds can be built by speakers on the fly based on a temporary, implicit relationship tying the two nouns, e.g., 'the guy taking a picture with a camera'.


This allows a more flexible comparison of the dissimilarity with respect to the alignment of the two series across time.

in (Emms and Kumar Jayapal, 2016), the model is evaluated qualitatively on the Google NGrams corpus (Michel et al., 2011), using a few manually selected target words.


It starts from the smallest sense (in proportion; rank first) and increases to the largest (rank last).

in this paper we propose an evaluation method based on largescale time-stamped annotated biomedical data, and a range of evaluation measures suited to the task.


A different model was proposed by Frermann and Lapata (2016), focusing instead on capturing the subtle meaning changes within a sense over time.

for every CUI, the corresponding MeSH descriptor is extracted from UMLS.


Figure 1: Distribution of the probabilities predicted by NEO and SCAN systems: the red distribution represents the predicted probability of the gold sense for every instance in the data; the blue distribution represents the highest predicted probability for every instance.

classification measures and MAE are susceptible to show complementary aspects of performance.


bringing human-generated questions into the proposed datasets is a challenge since human subjects would be required to possess a deep understanding of the different charts, before being able to ask reasonable and difficult questions.


Furthermore, English as source language produces higher numbers of ambiguous words compared to English as target language.

recent works suggest that the state-of-the-art Transformer architecture (Vaswani et al., 2017) for neural MT (NMT) is able to deal with lexical ambiguity quite well (Tang et al., 2018;Tang et al., 2019), learning to distinguish between senses during translation with high precision.


Guo et al. (2019a) convert the source token to target token with phrase table or embedding mapping for alignments. Ren et al. (2019) predict the duration (the number of mel-spectrograms) of each phoneme

we apply this alignment constraint for ASR only in the training stage.


Past work requires these constraints and the HSMM, when trained without them, does poorly, learning to capture diversity across videos rather than to identify steps.

a given step for a task can occur multiple times, or not at all, in any of the task's videos.


Our weakly-supervised results show that: (1) Both action segmentation metrics -all label accuracy and step label accuracy -are important to evaluate whether models adequately distinguish meaningful actions from background.

(3) ordering supervision alone is not sufficient to allow these models to learn better segmentations than a simple baseline that just uses the ordering to assign labels (ordered uniform); narration is also required.


Based on the predictions from these systems 5 , the relevant candidate words are highlighted so as to assist the annotator by inviting her attention to those words.

the presence of an advanced discipline of phonetics in Sanskrit formalises this euphonic assimilation of phones as Sandhi (Goyal and Huet, 2016).


In this paper, we attempt to address this problem by incorporating an affective lexicon as numerical influence values into affective neural network models through the framework of the Affect Control Theory (ACT).

by default, the hyper-parameters α, β and γ in Formula 1 and 2 are set equivalently to 0.33, and a is experimentally set to 1.15 as the optimized setting.


This section probes into the individual role of each attribute to the performance improvement.

attention weights are normally obtained using local context information.


We hypothesise that this is because it is better to build up representations with local context before exploiting long-range correlations.

this gives evidence that we do not need to store many separate representations for long-range memory to perform well at test time, but the approach does require storing them during training -and incurs significant slowdown to the model.


Comparatively larger resources, however, exist for German literature—Krug et al. (2017) create a dataset of coreference annotations between character mentions in German novels, which helped assess a rule-based system for coreference resolution (Krug et al., 2015).


This poses a challenge not only for users who want to learn CQL, especially when their background is in the humanities, but also for experienced users.

the created subcorpus can keep the user-specified proportion of the main registers without the need to specify individual texts.


Vowel length errors occur, when a vowel length mark is present in a wordform, but is missing in the corresponding stem or suffix in lexc.

adjectives inside the noun phrase do not agree with the noun, but may be used with nominal suffixes in case of ellipsis.


It's a fundamental component in many downstream tasks, and has been greatly advanced by deep neural networks (Lample et al., 2016; Chiu and Nichols, 2016; Peters et al., 2017).

in this paper, we propose a teacher-student learning method to address such limitations, where NER models in the source languages are used as teachers to train a student model on unlabeled data in the target language.


Our proposed teacher-student learning method then uses those K source-language models as teachers to train an effective student NER model for the target language on its unlabeled data D tgt .

for each experiment, we conduct 5 runs and report the average f1-score.


Results show BERT and RoBERTa coupled with temperature scaling achieve low ECEs in-domain, and when trained with label smoothing, are also competitive out-of-domain.

optimal temperature scaling values are bounded within a small interval.


In most cases, MLE models do not perform well on out-of-domain datasets, with ECEs ranging from 8-12.

table 6: Out-of-the-box calibration development set results for in-domain (SNLI, QQP, SWAG) and out-ofdomain (MNLI, twitterPPDB, HellaSWAG) datasets using pre-trained models.


For example, 1.89 on the top right explains how much information gain was achieved using DBpedia in comparison to Crunchbase.

our goal is to provide an evaluation data set that considers each of our listed limitations and can be used with ease by other developers as well, therefore allowing for a broader spectrum of application.


In some cases, for example year, more than one entity is possible

annotation methods can only be evaluated on a small part of the KG concerning a specific domain.


Five product domains with the largest number of QA pairs are selected, namely, Electronics, Home and Kitchen, Sports and Outdoors, Health and Personal Care, and Cell Phones and Accessories, con- Community Votes stituting around 2.7 million QA pairs in total.

one closely related work in the CQA context is a recent attempt of investigating the fact checking task in QA settings (Mihaylova et al., 2018), which was later adopted as the SemEval-2019 Task 8 (Mihaylova et al., 2019).


An agreement-matching strategy is then employed to model the self-coherence of the evidence sentences for obtaining reliable combined evidence embeddings to verify the answer verdict.

methods involving small datasets often use hand-crafted features to represent the claim (Mihaylova et al., 2018).


As pointed out in Mihaylova et al. (2019), verifying the verdict of answers in CQA requires using rich world knowledge.

for the evidence agreement matching module, we perform a grid search over n a and d a with the following hyperparameters where the final setting is underlined: n a = [2, 3, 4] and [64,128,256].


Recently, a new shared task, namely SemEval-2019 Task 8 (Mihaylova et al., 2019) investigates the fact checking problem in question answering scenario, requiring a system to classify the veracity of answers in a web forum.

in E-commerce scenario, product descriptions from the manufacture and user reviews from the former buyers contain rich product information, which can be treated as the candidate information pool for the retrieval process.


A possible drawback of our approach is that it only relies on entities, which do not fully cover the sentence semantics.

finally, performing outlier-aware meta-evaluation which was recently shown to be important in such settings (Mathur et al., 2020) could be beneficial.


Recently, large-scale pretrained language models (LM) such as BERT (Devlin et al., 2019) and GPT-2 (Radford et al., 2019) are used to improve the performance of dialog models, however in the cost of tens-fold larger model sizes and computations.

remarkably, LABES-S2S is optimized under the principled variational learning framework.


A two words of word-pair (wi , wj) belong to the same aspect term.

we can see that the key opinon pair extraction of aspect term and opinion term is still accomplished in pipeline and their approach also suffers from error propagation.


They take a word embedding as input and generate a definition of the word.

we first compute the representation H D =h 1:T of the definition D = d 1:T with a bidirectional LSTM network.


NRC Model Similar to the GloVe-BiLSTM Model, this model is LSTM-based.

we show that emotional representations can be successfully used to capture the underlying characteristics of sentences to suggest proper fonts.


This motivates us to explore font associations with regular users in a crowd-sourced setting.

5 GloVe-BiLSTM Model In this model, we use GloVe embeddings (Pennington et al., 2014) as input and a BiLSTM layer to encode word sequence information in forward and backward directions.


For example, when scoring the likelihood of the triple (person1, speaks languages, french), we allow the model to use a person's nationality.

we calculate the scores of the positive triples using Equation 1 (shown in Box 2a), and then for each positive triple calculate the scores of N negative triples, with negatives created by randomly permuting the entities on either side; we permute the right hand side (rhs) of T 1 with N = 2 in Box 3 of the example figure.


The scalar weight w KL controls the emphasis the model puts on debiasing vs. the original prediction task.

this conclusion is mirrored in table 6, which shows the accuracy of a neural network trained to predict the sensitive attributes from the trained embeddings.


As a result, even as we increase w KL to 100.0, the difference in scores approaches an asymptote.

for Wikidata, enough sensitive information remains to be able to predict some of the attributes very accurately.


Cao and Clark (2019) leverage constituency parsing for generation.

we lowercase and tokenize our system outputs to have fair comparisons with previous systems.


TOD-BERT is initialized from BERT, a good starting parameter set, then is further pre-trained on those task-oriented corpora.

the slot accuracy individually compares each (domain, slot, value) triplet to its ground truth label.


Naive Bayes, Random Forest and Neural Network classification models were trained on the extracted text and evaluated on a manually annotated dataset to ascertain the models ability to generalize on unseen data.

additionally, we evaluate event extraction over the corpus using the DaNIEL system.


The local context layer allowed the model to emphasize semantic-relative contextual words.

the input sentence is converted to the format "[CLS]" + Input sequence + "[SEP ]".


CDW helps to boost the performance of LCFS-ASC model more than the CDM.

the aspect sentiment classifier was unable to account for the syntactical correlation between aspect terms and the context words.


The aim of the task is binary sentiment classification, where each review document is classified into positive or negative sentiment.

Additionally, we compare with the start-of-the-art baseline LAPS (Ahuja et al., 2020), which relies on external task-specific cross-lingual parallel data (Ahuja et al., 2020), i.e., productto-product and query-to-query correspondences among all 5 languages.


To bridge the gaps between languages, numerous CLT algorithms have emerged, ranging from early translation-based methods (Prettenhofer and Stein, 2010), cross-lingual word representation learning (Conneau et al., 2018a), to powerful mPLM (Devlin et al., 2019;Lample and Conneau, 2019), from which the versatile multilingual representations derived suffice it to become a mainstream approach for various downstream CLT tasks.

another research efforts made on cross-lingual word representation learning (Zou et al., 2013;Mikolov et al., 2013;Conneau et al., 2018a;artetxe et al., 2018) and mPLM (Devlin et al., 2019;Lample and Conneau, 2019;Eisenschlos et al., 2019;Chidambaram et al., 2019), which exploit unsupervised learning on large-scale multilingual corpus to learn versatile multilingual contextualized embeddings.


In the meta graph G, each instance is regarded as a node.

existing mPLM-based methods focus on designing costly model pre-training while ignoring equally crucial downstream adaptation.


Besides, human studies show that our approach is indeed beneficial for preserving more concepts and relations from input graphs.

our approach: i recommend you to go to see your doctor too.


A potential solution for this issue is improving the training signal to enhance preserving of structural information.

applying Loss 1 alone achieves an improvement of 1.36 BLEU points, and Loss 2 alone obtains 0.66 more points than Loss 1.


For more detail, we remove any abbreviations from a KG node (such as "New York (NY)" is changed to "New York"), before finding the first phrase in the sentence that matches the longest prefix of the node.

as illustrated in the slashed blue box of Figure  2, the result contains several labeled arcs, each connecting a word pair (such as "wants" and "boy").


JamSpell and Pyenchant spelling checkers are inadequate for correcting text errors at character-level, these performed equally poorly, introducing significantly more errors than they fixed.

because of the difficulty in logging real wikitext errors introduced by human editors, we developed a sub-system that artificially can add human-like editing errors to the original text and convert it to training data.


Here, 'dictionary' refers to the word-list in the mapping table.

chinese NLP practitioners apply script converters 1   dataset into their desired language.


Simplified Chinese to Traditional Chinese character conversion is a common preprocessing step in Chinese NLP.

the LIHKG posts are shorter and forums titles are generally one sentence each (average length of nearly 50 characters).


In each conversation, a crisis counselor's highlevel goal is to guide the texter towards a calmer mental state.

in general, the directions of the effects we observe hold with stronger effects if we do not take this split.


A texter may start to counterproductively rehash or ruminate on their concerns (Nolen-Hoeksema et al., 2008; Jones et al., 2009); indeed, prior psychological work has highlighted the thin line between productive reflection and rumination (Rose et al., 2007; Landphair and Preddy, 2012).

our aim was to characterize behavior in typical conversations rather than exceptional cases or those that reflected earlier versions of the training curriculum.


Most of the successful and predominant methods for Bilingual Lexicon Induction (BLI) are mapping-based, where a linear mapping function is learned with the assumption that the word embedding spaces of different languages exhibit similar geometric structures (i.e., approximately isomorphic).

the MUSE dataset consists of Fasttext monolingual embeddings of 300 dimensions (Bojanowski et al., 2017) trained on Wikipedia monolingual corpus and gold dictionaries for 110 language pairs.


We will conduct a detailed analysis in the next section.

we use the encoded context hctx given by fctx(bt) from the dialogue state tracking model as query vectors, and attempt to recover the dialogue state from the memory component.


We choose the embedding dimensionality d among {50, 75, 100, 150, 200}, the hyperparameters α and β in [0.01, 1.0].

such an approach relies on system action annotations which are expensive to obtain.


To further enhance the generalization capability and boost the learning efficiency, we consider re-use the memory component for conditioned response generation.

memory slots with higher probability indicate that the corresponding words are expected to be more salient to represent the system utterance.


To obtain compact action representations, we propose an auxiliary task based on pseudo parallel corpus, i.e., dialogue context and state annotation pairs.

system utterances with the same intention might not share similar wordings, and existing attribution approaches can only identify salient words within utterances.


For targets that are not already in-domain for ROBERTA, our experiments show that contin-ued pretraining on the domain (which we refer to as domain-adaptive pretraining or DAPT) consistently improves performance on tasks from the target domain, in both high-and low-resource settings.

further adding to the realistic approach of the characters in this show is the depth of their personalities -These are dangerous men, most of them murderers, but by God if you don't love them too.


In MSA corpora, this has always to be considered a noun or an adjective.

regarding adjectives, there is a tie between both languages, as the number of adjectives in both Arabic and English is roughly similar.


The number of texts relates to the document sort making up the corpus, i.e. abstracts, full texts, sentences or other.

both groups are internally ordered chronologically by first release of the corpus or their publication year.


Each model training took 4 to 5h on an NVIDIA GeForce RTX 2080.

the training time can be significantly reduced by using the default number of epochs (3) which, in turn, lowers the amount of training time to less than one hour per run.


While lexical frequency effects are difficult to control in unconstrained purely spontaneous language production, language produced during the picture description task is much more constrained in that the picture provides a fixed set of objects, attributes, and relations that serve as referents for the the person describing the picture.

This is consistent with recent work by Weiner et al. (2018) that found diminished perplexity was of some (albeit modest) utility in predicting transitions to AD


The results demonstrate that the association between perplexity and lexical frequency is significant and positive for the control LM (coeff: 0.563, p < 0.001) and negative for dementia LM (coeff: -0.543, p < 0.001).

it is currently unclear as to whether this level of accuracy is due to dementia-specific linguistic markers, or a result of markers of other significant differences between the case and control group such as age (x¯ = 71.4 vs. 63) and years of education (x¯= 12.1 vs. 14.3) (Becker et al., 1994)


Unlike most written languages in the world, the Chinese writing system does not use explicit delimiters (e.g., white space) to separate words in written text.

it has not been fully explored what would be the best way of representing contextual information such as wordhood features in neural CWS models.


Then for each input character, the memory module addresses all the n-grams in the key list that contain the character and uses their corresponding values to generate an output vector to enhance the decoder for assigning a segmentation label to the character.

we use our best performing BERT-based model, i.e., BERT-CRF, with the n-gram lexicons constructed by the aforementioned three measures and run it on all benchmark datasets.


Several weakly-supervised methods address this problem by using a few keywords per aspect as supervision to guide the learning process.

s1, on the other hand, does not address the target with plain and general words, but instead use more specific words like "semi-private" and "date" which are uniquely used when people feel good about the ambience instead of other aspects.


The increases in loss are recorded in Table 5, which shows that [Next,Prev] patterns are most important, followed by [First] and [Period], while [Delim] isn’t very useful

we find that RoBERTa-AG is very robust and does not need much tuning.


We test the proposed approach with three benchmarks including the Ubuntu Dialogue Corpus (Lowe et al., 2015), DailyDialog (Li et al., 2017), and PERSONA-CHAT .

the last two columns of the tables compare different models in terms of parameter size and decoding speed.


Align 2, on the other hand, is what we expect an aligner to produce in order to have meaningful FER and DER evaluations.

we use two sets of weights for finding an alignment between the reference and the integrated model output.


The problem that arises
here is that since many disfluent words are copies
of fluent words, if the same cost is used to align
fluent and disfluent words, the alignment will be
ambiguous (i.e. there will be multiple alignments with the same cost).

as shown in Table 1, the alignment cost is slightly higher for inserting, copying and substituting a disfluent word and slightly lower for deleting a disfluent word.


Self-training and ensembling have also shown to provide benefit to disfluency detection (Jamshid Lou and Johnson, 2020).

the end-to-end model sometimes fails at detecting repetitions which are the most common type of disfluency.


In addition to its conceptual simplicity, our experiments show that ILM enables off-the-shelf LMs to infill effectively.

then, we compute PPL only on the tokens which comprise the masked span, i.e., PPL is computed for all models on exactly the same set of tokens.


Language models are (1) capable of generatingremarkably coherent text (Zellers et al., 2019; See et al., 2019), (2) efficient at generating text, and (3) conceptually simple, but cannot infill effectively as they can only leverage context in a single direction (usually the past).

we can thus cast infilling as learning p(y |x) without loss of generality.


Having access to candidate NPs during inference alone improves the performance on SuperGLUE WSC.

for WSC and use spaCy to mine candidate NPs.


We compare our results to the FREQ and SENSE baselines, and to the best results obtained by Cocos et al. (2018) who use information obtained from lexico-syntactic patterns, a lexicon annotated with intensity (SO-CAL) (Taboada et al., 2011), and paraphrases from PPDB.

more details about this filtering are given in Appendix A.


To answer the question whether or not we can recognize the structure in obituaries we formulate the task as sentence classification, where each sentence will be assigned to one of the eight classes we defined previously.

we define the following eight classes: Personal information, Biographical sketch, Characteristics, Tribute, Expression of gratitude, Family, Funeral information, and Other to structure obituaries at the sentence level.


We suspect that error propagation from antecedent-ranking may downgrade the quality of refinement.

it demonstrates that the benefits from HOi is diminished because the effects are two-sided: there are roughly same amounts of links (about 1%) becoming correct or wrong after HOi, therefore neither HOi method leads to much improvement overall.


SemCor alone attains 69.2 points, 1.3 points less than Clustercont.

indeed, language models like BERT (Devlin et al., 2019), RoBERTa , XLNet (Yang et al., 2019), etc., enable architectures built on top of them to attain performances that were previously out of reach (Wang et al., 2019).


We collect first-person perspective stories in three stages on Amazon Mechanical Turk (MTurk), using a pairing mechanism to account for topical variation between imagined and recalled stories.

we find no significant differences in demographics between the authors of imagined and recalled stories, 12 but authors of imagined stories scored slightly higher on measures of creativity and openness to experience (Cohen's d = 0.08, p = 0.01).


(TONE, positive and negative emotion) and evoke more cognitive processes.

we found no other significant associations with TIMESINCEEVENT.


With development and test F 1 scores of 83.7% and 75.8%, respectively, our event tagger slightly outperforms the best performing model in Sims et al.

the proposed measures uncover differences between the initially recalled and later retold stories that mirror the differences found between recalled and imagined stories (Table 2).


The goal of these checks is to ensure that the final hypernym graph produced by the OMW is a directed acyclic graph.

finally, after the Global Wordnet Association decided to adopt CILI, OMW became the natural place to coordinate it.


The DTD can be easily used to check if the XML is well-formed, which can help projects create their wordnets.

figure 1 shows a screenshot of CILI concept i46645, using a test version of the OMW interface with only six wordnets uploaded.


These include the presence of a unique English definition, and the fact that these new concepts link to the CILI hierarchy through an adequate semantic relation.

we decided to try to shift this burden, as far as possible, to the individual wordnet projects.


Notable extensions include: confidence, corpus frequency, orthographic variants, lexicalized and non-lexicalized synsets and lemmas, new parts of speech, and more.

this is a much more manageable goal, and projects receive the immediate gratification of seeing their projects (or newer versions of their old projects) updated almost immediately.


This process is illustrated in Figure 2.

it obtains a lower pairwise score, but to the detriment of a lower mBLEU score.


We found that workers had difficulty identifying the correct answer because the number of options for labels in SRL can be overwhelming and workers lack the linguistic expertise to handle subtle cases.

the predicate word and argument spans are automatically identified using the Akbik and Li (2016) system.


We present the workers with spans by projecting the head-word, as we expected spans to be more intuitive for workers.

we would like to thank Laura Burdick for helpful feedback on earlier drafts of this paper and the anonymous reviewers for their helpful suggestions.


An important goal for building systems that learn and generalize like people is to engineer systems with inductive biases for the right degree of systematicity.

We make use of the Natural language inference (NLI) task to study the question of systematicity.


There has been much interest in the problem of systematic generalization in recent years (Bahdanau et al., 2019; Bentivogli et al., 2016; Lake et al., 2017a,b; Gershman and Tenenbaum, 2015; McCoy et al., 2019a; Veldhoen and Zuidema, 2017; Soulos et al., 2019; Prasad et al., 2019; Richardson et al., 2019; Johnson et al., 2017, inter alia).

our analyses contain two ideas that may be useful for future studies of systematicity.


Finally, Kim et al. (2019) explore pre-training schemes' abilities to learn prepositions and wh-words with syntactic transformations (two kinds of closedclass words which our work does not address).

our probes combine novel open-class words with familiar closed-class words, to test whether the closed-class words are treated systematically by the network.


Our implementation is based on the Transformer library (Wolf et al., 2019).

it fails to predict S1 as the supporting facts, while HGN succeeds, potentially due to the explicit connections between sentences in the constructed graph.


In each step estimating the Hessian-vector product, we took a batch of 8 training examples for stability.

we posit that influence functions may be a more suitable approach to interpreting models for such relatively complex natural language 'understanding' tasks (while simpler attribution methods like gradients may be sufficient for tasks like sentiment analysis).


While Koh and Liang (2017) show that influence functions can be a good approximation even when the convexity assumption is not satisfied (in their case, a CNN for image classification), it is still not obvious that the influence function would work for BERT.

McCoy et al. (2019) hypothesize that the main artifact NLI models might learn is lexical overlap.


We see one potential problem out of the above method: it can only be applied to a certain group of examples and imply a general model behavior by examining the prediction imbalance.

furthermore, we develop a new quantitative measure based on influence functions that can reveal artifacts in training data.


It is worth noting that influence functions are guaranteed to be accurate only when the model is strictly convex (i.e., its Hessian is positive definite and thus invertible) and is trained to convergence.

we are interested in the use of influence functions (Koh and Liang, 2017), which are in a sense inherently 'faithful' in that they reveal the training examples most responsible for particular predictions.


All of the updated models still preserve the utility in recognizing the overt toxicity.

table 2 shows the performance of the models improved by each method.


The authors argue that the lack—or the overabundance—of context information prevents annotators from making a reliable assessment, which leads to noisy and uncertain annotations.

induced by our interpretation of the task.


Finally, if the input sentence is already in the target style, our model won't add any stylistic markers and thus would allow the input to flow as is.

mETEOR also uses synonyms and stemmed forms of the words in candidate and reference sentences, and thus may be better at quantifying semantic similarities.


Our method has two variations: Intra-LADA and Inter-LADA.

data point would be augmented through Intra-LADA with a probability π and Inter-LADA with a probability 1 - π.


For Intra-LADA, as it broke the sentence structures, it cannot be applied to Flair that was based on LSTM-CRF.

in practice, we set the probability 0.3, and kept the settings for each kind of LADA the same.


Loss on the Development Set To illustrate that our LADA could also help the overfitting problem, we plotted the loss on the development set of BERT, BERT + Inter-LADA and BERT + Semi-Inter-LADA on CoNLL and GermEval training with 5% labeled data in Figure 3.

both contain one and only one entity (Hendrix) of the same type (Person).


These techniques can help us obtain x' and x'' more efficiently

at the pre-training stage, they are trained on a huge amount of unlabeled data in an unsupervised manner, e.g., T5 is pre-trained on 745 GB text.


As we discussed in Section 1 and 2, the output probability of a calibrated model should reflect the true likelihood.

For indistribution data, a well-calibrated model is expected to output prediction confidence comparable to its classification accuracy.


We instead rely on the observation that as we increase p in NS@p to make generation more diverse, the cardinality of N also goes up, on average, and so does the probability P (GT 2 N) that N contains the GT token.

entropy is an unbounded measure, and has a non-linear inverse growth relative to our proposed accuracy metric, which makes their mathematical combination difficult.


To improve performance at different data sizes we set the mini-batch size to 6K tokens for the 30K-sentence datasets, 12K tokens for 0.5Msentence datasets, and 24K for the remaining larger datasets (Popel and Bojar, 2018).

to the best of our knowledge, this problem has not yet been directly addressed in the NLG setting.


The scarcity of the training corpus is a common problem for many language pairs, which might lead to the NMT model's poor performance.

the evaluation metric is BLEU (Papineni et al., 2002).


We also tried visual features extracted with a neural object recognizer (Simonyan and Zisserman, 2014) which only give a small improvement over colour histograms.

in human cognition, world knowledge supports the perception of object colours: knowing that trees are typically green helps to perceive their colour in certain contexts.


For example, in a database of basketball game results, the predicate "A wins" is equivalent to "scoreA > scoreB" according to common sense.

we report the mean / std / max of these two statistics among 21 dev set submissions.


We focus on the queries from the 21 submissions that are considered incorrect by ESM but correct by our test suite evaluation, randomly sampled and manually examined 100 of them.

the current Spider metric leads to a 2.5% false negative rate on average and 8.1% in the worst case, indicating that test suite accuracy is needed.


Hence a response can be acceptable to human annotators even if it does not align well with the reference either in terms of word-overlap or semantic embedding.

to prior art, our means of evaluation captures not only the quality of generation, but also the diversity and logical consistency of responses.


Then, we feed the sentence with erroneous span annotations to a seq2seq model for ESC.

table 3: In-depth time cost (in second) analysis in CoNLL-14 which contains 1,312 test sentences.


Same issue happens with the case of 1-to-N and N-to-1 predictions.

on WN18RR our results achieve the new state-of-the-art performance.


However, these knowledge graphs need to be updated with new facts periodically.

The hyper-parameters of our model are tuned by grid search during training process, including learning rate, embedding dimension d and sub-embedding dimension d s .


Distance-based knowledge graph embeddings have shown substantial improvement on the knowledge graph link prediction task, from TransE to the latest state-of-the-art RotatE.

we examine the proposed model in terms of the following aspects: Impact of sub-embedding dimension: we fix the embedding dimension as 400, and increasewe examine the proposed model in terms of the following aspects: Impact of sub-embedding dimension: we fix the embedding dimension as 400, and increase the subembedding dimension d s from 2 to 20, the MRR of OTE is improved from 0.327 to 0.355 (See Row 3 and Row 4).3 and Row 4).


Our English-Spanish models show a similar behaviour to the one observed for the English-Italian pair.

while the original XLM operates on streams of text, split by sentence separators, we split the stream of tweets, so that each example contains only one tweet.


Section 5 reports on the evaluation results, while Section 6 summarizes our findings.

since emojis are not always present in each tweet, we adopt a hybrid approach: when emojis are not present, the previously described MLM objective is trained.


Our quantization scheme was chosen to be uniform, meaning that the step size between two quantized values is constant.

we proposed a full quantization strategy for the Transformer architecture.


A few variations to this additive attention mechanism have been proposed, such as multiplicative and self-attention (Luong et al., 2015; Cheng et al., 2016; Lin et al., 2017).

numerical values can be represented using fewer bits (Tang and Kwan, 1993; Marchesi et al., 1993).


We consider a relation to be hierarchical when its corresponding graph is close to tree-like (low curvature, high Khs G ).

it provides an analogue through the lens of parallel transport: given two points x, y and a vector v in T c x , there is a unique vector in T c y which creates the same angle as v with the direction of the geodesic (shortest path) connecting x to y.


In Transformer-based models, the multi-head attention explores the relationships among tokens by calculating the token similarities in implicit feature spaces.

bertSUM: Road Home, Louisiana grant program for homeowners who lost their houses to hurricanes Katrina and Rita, is expected to cost far more than $7.5 billion provided by Federal Government, in part because many more families have applied than officials had anticipated.


In the next section, we describe the data sets we use for the experiments.

there are still a number of questions remaining about how well such models really work for real applications, and how much data is needed to achieve acceptable performance.


Wikipedia contents describing news media were useful for predicting the political bias and the factuality of these media (Baly et al., 2018a).

given the scale of the proliferation of false information online, it became clear that it was unfeasible to fact-check every single suspicious claim, even when this was done automatically, not only due to computational challenges but also due to timing.


As a few-shot natural language generation model, diversity is a very important indicator for quality evaluation.

b4 fails to transfer R4 into the movie domain.


Our method answers 56% of questions while maintaining 80% accuracy; in contrast, directly using the model's probabilities only answers 48% at 80% accuracy.

for each, we sampled 100 errors in which the system confidently gave a wrong answer (overconfident), and 100 errors in which the system abstained but would have gotten the question correct if it had answered (underconfident).


Figure 3a shows that MaxProb values are generally lower for OOD examples than in-domain examples, following previously reported trends (Hendrycks and Gimpel, 2017; Liang et al., 2018)

45% of underconfidence errors are due to the passage requiring coreference resolution over long distances, including with the article title.


Convolutional Encoder Inspired by the recent trend of replacing recurrent networks with CNNs (Dauphin et al., 2016; Yu et al., 2018a) and Transformers (Vaswani et al., 2017; Devlin et al., 2019) for sequence modeling, we use positional encoding (PE), CNNs, and layer normalization (Ba et al., 2016) to build our basic encoding block.

comprehensive ablation studies demonstrate how each of our annotations and model components helps to improve the performance of the tasks.


This model is pretrained on a large amount of image-text pairs from multiple image captioning (Lin et al., 2014; Krishna et al., 2017) and image question answering (Goyal et al., 2017; Hudson and Manning, 2019; Zhu et al., 2016a) datasets.

second, sTAGE computes attention scores from each QA word to object regions and subtitle words.


Temporal and Spatial Attention Attention has shown great success on many vision and language tasks, such as image captioning (Anderson et al., 2018;Xu et al., 2015), visual question answering (Anderson et al., 2018;Trott et al., 2018), language grounding (Yu et al., 2018b), etc.

it is also of reasonable size compared to the grounded video captioning dataset ANet-Entities (Zhou et al., 2019).


The results presented in table 4 show that the most influential is decision whether to correct morphosyntactic annotation of a token or not.

finally, in section 6. we discuss the preliminary interannotator agreement results of two phases of annotation calculated for a small fragment of the corpus that is already annotated by two linguists.


Typical MWEs may be compositional (e.g. dawka śmiertelna ‘deadly dose’ is a ‘dose’) or not (e.g. there is not a meaning for centrum ‘centre’ for centrum handlowe ‘shopping centre’, ‘mall’

this paper examines the procedure for lexico-semantic annotation of the Basic Corpus of Polish Metaphors that is the first step for annotating metaphoric expressions occurring in it.


Then, we segment word senses into four portions with a reasonable density (> 10% of the test set) and ensure that senses with the same number of training instances are in one segment.

when using word-specific classifiers, the sparseness of annotations leads to inferior sense disambiguation performance on less frequently seen words.


Following Kudugunta et al. (2019), we perform SVCCA on the encoder final outputs mean-pooled over timesteps, using a multiparallel evaluation set.

Default: max 2400 tokens/pair, 0.2 dropout.


Given a review sentence S, we first tokenize it using the WordPiece vocabulary (Wu et al., 2016) and add tokens [CLS] and [SEP] to the beginning and the end of the tokenized sentence, respectively.

besides, HAST+RD, IMN+RD and RINANTE+RD, which utilize the aspect and opinion term co-extraction models, achieve better performances than DE-CNN+RD.


For example, if 'overrated' is used to modify 'pizza', this relation could provide guidance to extract the aspect 'pizza' and the opinion expression 'overrated'.

then, the probability of the predicted sequence Y t can be calculated as follows: where Y t X denotes all possible label sequences.


A common template about the general structure of user explanations is reflected from the parameter values.

for this, crowdsourced workers on Amazon Mechanical Turk were asked to demonstrate how to complete these tasks and provide stepwise explanations to an AI assistant on how to complete the task.


Today's conversational assistants such as Alexa or Cortana act on a small number of preprogrammed language commands (e.g., "What is the weather going to be like?").

f1i = ? denotes that feature i was not relevant.


We further demonstrate that using a diversity of prompts through ensembling further improves accuracy to 39.6%.

while this paradigm has been used to achieve a number of intriguing results regarding the knowledge expressed by LMs, they usually rely on prompts that were manually created based on the intuition of the experimenter.


Previous work has approached generating coherent utterances in conversations through encouraging the model to learn similar distributed representations throughout the conversation (Baheti et al., 2018; Xu et al., 2018; Zhang et al., 2018a).

is phrased rather vaguely, the QA model is able to identify its correct answer from the paragraph The band released their third album, True, in March 1983, which offers new information (note the answer in the figure only reflects the actual human-human conversation, not this hypothetical one).


This corpus contains 5,800 sentences pairs from news sources on the web, human-annotated as either paraphrases or unrelated.

for each sentence pair, the sentence similarity models are used to estimate the similarity score between the second sentence of the pair and all available replacement sentences.


We generate a ground truth by randomly choosing sentence pairs from the SPAADIA and Switchboard corpus respectively, and annotate whether they share a dialogue act.

a valid replacement such as this one has to be chosen from a list of available sentences to successfully complete the sentence swapping task.


During deployment, none of the machine learning based approaches needs additional data.

in the following, a short overview of the chosen approaches as well as a discussion of their characteristics is given.


They find that the ability to identify contextual paraphrases is of great importance to the overall performance of the approach.

in our work, we include a greater number of sentence pairs in our understanding of paraphrases: two sentences are considered contextual paraphrases if they can fulfil the same function in the context of a conversation.


This constitutes a substantial improvement from the majority class prediction of 0.5.

in human-human interaction, we regularly express our intention with phrases that are vastly different regarding both word content and syntactic structure.


Some pages also contained tags to indicate that.

after the statement has been correctly translated, it can be checked using an automatic tool.


These results highlight the importance of both functions of the first-round re-ranker.

namely, queries are split into five equal-sized partitions.


As we mentioned in Section 3.1, we use an LSTM to store the historical path information.

besides, for every state s t , we also add an additional action (r LOOP , e t ), where LOOP is a manually added selfloop relation.


Based on this strategy, the dynamic completion strategy introduces some additional actions during the reasoning process to increase the number of paths, which can alleviate the sparsity of KGs.

the model using the DC strategy alone performs better than the model using the DA strategy alone, which is predictable, since the DA strategy only allows the agent to make a correct choice, and will not substantially alleviate the sparsity of KGs.


The performance drops when we remove either one of them.

this means the ASR task is necessary for both the advanced courses and St.


Unless noted, we use N = 8 encoder blocks to perform the ASR and the FMLM pre-training tasks.

our method still outperforms the simple pre-training method by a large margin.


In this paper, we propose to use dice loss in replacement of the standard cross-entropy objective for data-imbalanced NLP tasks.

the number of tokens with tagging class O is 5 times as many as those with entity labels for the CoNLL03 dataset and 8 times for the OntoNotes5.0 dataset; Dataimbalanced issue is more severe for MRC tasks (Rajpurkar et al., 2016; Nguyen et al., 2016; Rajpurkar et al., 2018; Kocisky et al., 2018; Dasigi et al., 2019) with the value of negative-positive ratio being 50-200, which is due to the reason that the task of MRC is usually formalized as predicting the starting and ending indexes conditioned on the query and the context, and given a chunk of text of an arbitrary length, only two tokens are positive (or of interest) with all the rest being background.


We hypothesize that this is because LEXSUB learns the lexical relations in a linear subspace which happens to be the simplest possible manifold.

this embedding layer is initialized with the embedding that is to be evaluated and is not fine-tuned during training.


However, recent studies (Ghaeini et al., 2018; Jain et al., 2019; Wiegreffe and Pinter, 2019) have questioned the validity of attention as a faithful explanation of model's behavior.

this is a commonly used measure for input attributions (Shrikumar et al., 2016; Selvaraju et al., 2019).


This model uses the same sentence encoder as DirectSup but differs in the attention mechanism used to decide sentence importance.


We propose to use low-rank factorization as a less restrictive, yet powerful representation and obtain parameter reduction by pruning rank-1 components.

no factorization is used for this baseline.


Previous sections illustrate the potential external supervision by assuming the existence of vokens.

This low grounding ratio leads to low coverage of visual supervision in previous approaches (Frome et al., 2013; Kiela et al., 2018).


Using our proposed vokenizer with a contextualized token-image matching model, we generate vokens for English Wikipedia.

we introduce a "revokenization" technique to address this limitation.


A KB in this form is often called a Knowledge Graph (KG) (Bollacker et al.) due to its graphical representation

then, we use G and the image embedding, feed them to the vision-language module, and send its output to a classifier and check the answer.


It creates a joint representation in a single space of the three different embedding spaces.

this module alone is not able to answer questions that require insights that are neither in the image, nor in the question.


Danescu-Niculescu-Mizil et al. (2013) utilizes requests annotated for politeness to create a framework specifically to relate politeness and social power.

the annotation scheme underwent five revisions, each time with three different conversations, eventually yielding a high Cohen's Kappa score of 0.85 across all face acts (Cohen, 1960).


As can be seen, our models outperform strong comparison systems on both DUC test sets: QUERYSUM S achieves the best R-1 while QUERY-SUM P achieves the best R-2 and R-SU4.

to previous work, our proposal does not simultaneously perform segment selection and query matching.


Generally, we observed that the models were close to 30-40% in all categories meaning that they were sufficiently similar to the ground truth distribution of data (the neutral options were chosen more frequently).

Note that h, l, and a define the sizes of the encoder and decoder transformers individually.


For every goal configuration c derived by LTF or LTL, the AM dependency tree described by c is well-typed.

we also consider a transition system in which the lexical type is chosen after deciding on the outgoing edges.


We have made our code publicly available to facilitate future research.

Among other noteworthy works, API usage information (Hu et al., 2018b), reinforcement learning (Wan et al., 2018), dual learning (Wei et al., 2019), retrieval-based techniques (Zhang et al., 2020) are leveraged to further enhance the code summarization models.


In this work, we study an alternative of the relative position representations that ignores the directional information (Ahmad et al., 2019).

In our experiments, we observe that a deeper model (more layers) performs better than a wider model (larger d model).


Our focus in this paper is on the problem of extracting and interpreting content of tables characteristic to machine learning papers.

this interface allows annotators on Papers with Code to take a paper and label it with results tuples.


Table D.7: Average number of pre-terminal groups and their generating non-terminals.

we consider if the induced grammars reflect the correct word classes defined by the preterminals, and if they capture the simple hierarchy defined on top of these word-classes.


The dataset serves as the empirical basis for the lexicographic work on extending the Wortprofil and for the enrichment of GermaNet with new lexical relations.

we use a feed-forward neural network with one hidden layer of size 4 (hidden layer size was tuned on validation, see Appendix B) and a ReLU non-linearity and apply early stopping.


The selected preceding or succeeding ACC can neither overlap with a previously heuristically labeled ACC nor occur in a different sentence than the discourse marker that triggered its labeling.

major claims often follow the phrases in the first row of the major claim column.


They improved generalisation of the relation models by mapping context onto vector spaces.

a collection of 18,386 texts in total was selected for the annotation process.


They are all EP monolinguals, aged 3;2 to 11,05.

these generalizations often come with partially or completely untested assumptions about the irrelevance of potential noise in the data, the nature of which may influence results in ways which remain elusive to the researcher.


As an essential task in task-oriented dialog systems, slot filling requires extensive training data in a certain domain.

the Ct framework suffers from the difficulty of capturing the whole slot entity, while our framework is able to recognize the slot entity tokens by sharing its parameters across all slot types.


It utilizes the hierarchical relationship among query type, mention type and query intent to obtain the final intent, which is illustrated in Figure 4(a).

the loss function L mt is set to the cross entropy on {S mt i }.


The two auxiliary tasks can also help to improve the entity linking task.

there are no existing studies about intent detection on spatial domain queries.


"Select" means our unsupervised self-training method with grammaticality judgment model.

most previous disfluency detection work focuses on detecting reparandums.


It makes easy to validate the annotation and analyze system errors.

it is easy to find entailment pairs from the corpus.


Replacing nothing (nada) with anything (algo) is incorrect in Spanish.

a qualitative error analysis has shown that the methods applied are not optimal.


We are able to automatically tag large sets of users according to their stance of preset topics.

solely using the retweeted accounts as features has been shown to be effective for stance classification (Darwish et al., 2020;Magdy et al., 2016a).


In Section 5, we provide instructions for how programmers can extend our PYTHON package to include new corpora from TOOLBOX files or from the more than 130 corpora encoded in CHILDES CHAT, which are openly available in TalkBank.

in this paper, we have given an overview of the ACQDiV corpus database and aggregation pipeline, which is available as a PYTHON package via PYPi.


MOCHA contains human judgement scores on 40K candidates, an order of magnitude larger than prior work  (Chen et al., 2019).

lERC assigns a score that better reflects human judgement.


In our work, we use toxicity classification as one example to detect local group bias in texts and show that such local group bias could be caused by different topics in the texts.

to analyze and detect such local bias, we propose LOGAN, a new bias detection technique based on clustering.


Machine learning techniques have been widely used in natural language processing (NLP).

Other applications such as cross-lingual transfer learning (Zhao et al., 2020) and natural language generation (Sheng et al., 2019) also exhibit unintended biases.


For example, utterances belong to Wh-style Interrogative and Yes-no Interrogative may share some transferable word patterns while word patterns in Wh-style Interrogative and Exclamatory with interjections are totally different.

moreover, since mTL never sees responses in target sentence functions in training, the generated responses are not coherent with the given sentence function at all.


Here is how we apply MAML to response generation on infrequent sentence functions.

similar sentence functions trigger similar initializations and dissimilar sentence functions trigger different ones.


Figure 1 presents how sentence functions influence the responses.

the meta-learned model is not necessarily a good model on its own, but it adapts fast on any new task with a few gradient update steps.


The UD project requires each language to share their annotation specification with other treebanks of same language to increase consistency and parallelism.

three phrase structure parsers are available.


This is followed by a feed forward layer (g) and a dense layer to finally assign probabilities to each position (p) in the input utterance.

we overcome this by providing global supervision.


The time allotted for each task was 20 minutes and only a single submission per Turker was allowed.

after a preliminary investigation, we argue that this gap is acceptable.


Prior work has combined these three scores into a single number using geometric averaging (Xu et al., 2018) or learned weights (Pang and Gimpel, 2019).

Attribute transfer has been criticized for its limited real-world applications: Pang (2019) argue that semantic preservation is critical for author obfuscation (Shetty et al., 2018), data augmentation (Xie et al., 2019;Kaushik et al., 2020), text simplification (Xu et al., 2015), writing assistance (Heidorn, 2000).


We extend the result in Theorem 3 as follows.

we explore which languages can be decided by different encoder-decoder pairings.


If x starts with a, then we accept iff there exists a sequence of bs following the prefix of as such that both sequences have the same length.

see Appendix A for further discussion of this.


Then, we calculate the embedding vector of the k-th r-NE as the average of the vectors of the words consisting that r-NE: R = (r1, r2, . . . , rk, . . . , rK).

a node of type F has a tendency to be linked to bounding boxes for unprocessed ingredients (see Figure 2).


A recipe describes instructions for a dish.

we assume that each step in the recipes of our dataset has an image.


Given a procedural text with its image sequence and flow graph, annotators put a bounding box of an object in an image, which is linked to a node of the flow graph.

sequential bounding boxes and a flow graph will be useful for multimodal state tracking.


This is perfectly understandable given the complexity of such a reproduction, in particular in the multitask learning setting of end-to-end RE and often without (documented) source code.

we also use negative sampling by randomly selecting 100 negative spans during training.


This allows us to better model interactions between arguments and triggers.

next, we use that model to tag unlabeled data and get a much larger but noisy silver dataset (Sec. 3).


In terms of modeling, their approaches used regular BERT as their encoders, where the argument representations are not explicitly conditioned on triggers.

(2) Our model does not achieve the highest scores on AI.


It is reasonable to expect that better performing language models for individual people will result in better performing systems for these downstream tasks for individual people.

the interpolated probability for that type would be (p+0)/2, where p is the probability from the language model that contains that type.


Classification of the 3-class directness results in an UAR of 56 %, and the binary directness reaches an UAR of 75 %.

for the classification of the elaborateness, the SVM classifier performs comparable to the ANN classifier.


Self-attention sub-layer The attention mechanism can be formulated as querying a dictionary with key-value pairs.

the performance is evaluated by the accuracy.


For pre-training, we use the same data as BERT, which consists of 3.3 Billion tokens from Wikipedia and BooksCorpus (Zhu et al., 2015).

this also indicates that splitting the pruning for attention heads could have more flexibility for the model to find an optimal architecture.


Fan et al. (2020) applies random pruning to the entire layers.

the left column shows the percentage of pruned parameters.


However, individuals are less adherent to these norms as they perceive themselves to be anonymous or when interacting with an individual whom they perceive they will not interact with again (Rubin, 1983; Wynne and Wynne, 1986; Dindia et al., 1997); without the potential loss of face or social capital in such circumstances, individuals are more likely to engage in more intimate communication.

the authors conducted several rounds of pilot annotation trials among seven annotators, prior to beginning annotation for the current study's data.


Individuals in these encounters have little likelihood of future interactions, removing the consequences for violating intimacy norms around increased disclosure (Thibaut, 2017; Wynne and Wynne, 1986).

our models would allow tracking intimacy changes to separate offensive questions from those in conversations that gradually become more intimate.


Finally, in order to process long sequences, Xie et al. (2019) and Joshi et al. (2019) investigate simple approaches of truncating or partitioning them into smaller sequences, e.g., to fit within 512 token limit of BERT for classification; However, such partitioning leads to a loss of discriminative cross-partition information and is still computationally inefficient

table 4 shows data statistics of 5 datasets used in complementary + finetuning evaluation of our proposed topicBERt model via Document Classification task.