mtybilly/Instruct2DS huggingface dataset:
Meta Distant Transfer Learning for Pre-trained Language Models
Authors: Chengyu Wang, Haojie Pan, Minghui Qiu, Jun Huang, Fei Yang, Yin Zhang
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: With the wide availability of Pre-trained Language Models (PLMs), multi-task fine-tuning across domains has been extensively applied. For tasks related to distant domains with different class label sets, PLMs may memorize non-transferable knowledge for the target domain and suffer from negative transfer. Inspired by meta-learning, we propose the Meta Distant Transfer Learning (Meta-DTL) framework to learn the cross-task knowledge for PLM-based methods. Meta-DTL first employs task representation learning to mine implicit relations among multiple tasks and classes. Based on the results, it trains a PLM-based meta-learner to capture the transferable knowledge across tasks. The weighted maximum entropy regularizers are proposed to make meta-learner more task-agnostic and unbiased. Finally, the meta-learner can be fine-tuned to fit each task with better parameter initialization. We evaluate Meta-DTL using both BERT and ALBERT on seven public datasets. Experiment results confirm the superiority of Meta-DTL as it consistently outperforms strong baselines. We find that Meta-DTL is highly effective when very few data is available for the target task.
UniKER: A Unified Framework for Combining Embedding and Definite Horn Rule Reasoning for Knowledge Graph Inference
Authors: Kewei Cheng, Ziqing Yang, Ming Zhang, Yizhou Sun
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Knowledge graph inference has been studied extensively due to its wide applications. It has been addressed by two lines of research, i.e., the more traditional logical rule reasoning and the more recent knowledge graph embedding (KGE). Several attempts have been made to combine KGE and logical rules for better knowledge graph inference. Unfortunately, they either simply treat logical rules as additional constraints into KGE loss or use probabilistic model to approximate the exact logical inference (i.e., MAX-SAT). Even worse, both approaches need to sample ground rules to tackle the scalability issue, as the total number of ground rules is intractable in practice, making them less effective in handling logical rules. In this paper, we propose a novel framework UniKER to address these challenges by restricting logical rules to be definite Horn rules, which can fully exploit the knowledge in logical rules and enable the mutual enhancement of logical rule-based reasoning and KGE in an extremely efficient way. Extensive experiments have demonstrated that our approach is superior to existing state-of-the-art algorithms in terms of both efficiency and effectiveness.
Wasserstein Selective Transfer Learning for Cross-domain Text Mining
Authors: Lingyun Feng, Minghui Qiu, Yaliang Li, Haitao Zheng, Ying Shen
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Transfer learning (TL) seeks to improve the learning of a data-scarce target domain by using information from source domains. However, the source and target domains usually have different data distributions, which may lead to negative transfer. To alleviate this issue, we propose a Wasserstein Selective Transfer Learning (WSTL) method. Specifically, the proposed method considers a reinforced selector to select helpful data for transfer learning. We further use a Wasserstein-based discriminator to maximize the empirical distance between the selected source data and target data. The TL module is then trained to minimize the estimated Wasserstein distance in an adversarial manner and provides domain invariant features for the reinforced selector. We adopt an evaluation metric based on the performance of the TL module as delayed reward and a Wasserstein-based metric as immediate rewards to guide the reinforced selector learning. Compared with the competing TL approaches, the proposed method selects data samples that are closer to the target domain. It also provides better state features and reward signals that lead to better performance with faster convergence. Extensive experiments on three real-world text mining tasks demonstrate the effectiveness of the proposed method.
Jointly Learning to Repair Code and Generate Commit Message
Authors: Jiaqi Bai, Long Zhou, Ambrosio Blanco, Shujie Liu, Furu Wei, Ming Zhou, Zhoujun Li
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: We propose a novel task of jointly repairing program codes and generating commit messages. Code repair and commit message generation are two essential and related tasks for software development. However, existing work usually performs the two tasks independently. We construct a multilingual triple dataset including buggy code, fixed code, and commit messages for this novel task. We first introduce a cascaded method with two models, one is to generate the fixed code first, and the other generates the commit message based on the fixed and original codes. We enhance the cascaded method with different training approaches, including the teacher-student method, the multi-task method, and the back-translation method. To deal with the error propagation problem of the cascaded method, we also propose a joint model that can both repair the program code and generate the commit message in a unified framework. Massive experiments on our constructed buggy-fixed-commit dataset reflect the challenge of this task and that the enhanced cascaded model and the proposed joint model significantly outperform baselines in both quality of code and commit messages.
Inflate and Shrink:Enriching and Reducing Interactions for Fast Text-Image Retrieval
Authors: Haoliang Liu, Tan Yu, Ping Li
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: By exploiting the cross-modal attention, cross-BERT methods have achieved state-of-the-art accuracy in cross-modal retrieval. Nevertheless, the heavy text-image interactions in the cross-BERT model are prohibitively slow for large-scale retrieval. Late-interaction methods trade off retrieval accuracy and efficiency by exploiting cross-modal interaction only in the late stage, attaining a satisfactory retrieval speed. In this work, we propose an inflating and shrinking approach to further boost the efficiency and accuracy of late-interaction methods. The inflating operation plugs several codes in the input of the encoder to exploit the text-image interactions more thoroughly for higher retrieval accuracy. Then the shrinking operation gradually reduces the text-image interactions through knowledge distilling for higher efficiency. Through an inflating operation followed by a shrinking operation, both efficiency and accuracy of a late-interaction model are boosted. Systematic experiments on public benchmarks demonstrate the effectiveness of our inflating and shrinking approach.
On Pursuit of Designing Multi-modal Transformer for Video Grounding
Authors: Meng Cao, Long Chen, Mike Zheng Shou, Can Zhang, Yuexian Zou
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Video grounding aims to localize the temporal segment corresponding to a sentence query from an untrimmed video. Almost all existing video grounding methods fall into two frameworks: 1) Top-down model: It predefines a set of segment candidates and then conducts segment classification and regression. 2) Bottom-up model: It directly predicts frame-wise probabilities of the referential segment boundaries. However, all these methods are not end-to-end, i.e., they always rely on some time-consuming post-processing steps to refine predictions. To this end, we reformulate video grounding as a set prediction task and propose a novel end-to-end multi-modal Transformer model, dubbed as GTR. Specifically, GTR has two encoders for video and language encoding, and a cross-modal decoder for grounding prediction. To facilitate the end-to-end training, we use a Cubic Embedding layer to transform the raw videos into a set of visual tokens. To better fuse these two modalities in the decoder, we design a new Multi-head Cross-Modal Attention. The whole GTR is optimized via a Many-to-One matching loss. Furthermore, we conduct comprehensive studies to investigate different model design choices. Extensive results on three benchmarks have validated the superiority of GTR. All three typical GTR variants achieve record-breaking performance on all datasets and metrics, with several times faster inference speed.
COVR: A Test-Bed for Visually Grounded Compositional Generalization with Real Images
Authors: Ben Bogin, Shivanshu Gupta, Matt Gardner, Jonathan Berant
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: While interest in models that generalize at test time to new compositions has risen in recent years, benchmarks in the visually-grounded domain have thus far been restricted to synthetic images. In this work, we propose COVR, a new test-bed for visually-grounded compositional generalization with real images. To create COVR, we use real images annotated with scene graphs, and propose an almost fully automatic procedure for generating question-answer pairs along with a set of context images. COVR focuses on questions that require complex reasoning, including higher-order operations such as quantification and aggregation. Due to the automatic generation process, COVR facilitates the creation of compositional splits, where models at test time need to generalize to new concepts and compositions in a zero- or few-shot setting. We construct compositional splits using COVR and demonstrate a myriad of cases where state-of-the-art pre-trained language-and-vision models struggle to compositionally generalize.
Vision-and-Language or Vision-for-Language? On Cross-Modal Influence in Multimodal Transformers
Authors: Stella Frank, Emanuele Bugliarello, Desmond Elliott
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Pretrained vision-and-language BERTs aim to learn representations that combine information from both modalities. We propose a diagnostic method based on cross-modal input ablation to assess the extent to which these models actually integrate cross-modal information. This method involves ablating inputs from one modality, either entirely or selectively based on cross-modal grounding alignments, and evaluating the model prediction performance on the other modality. Model performance is measured by modality-specific tasks that mirror the model pretraining objectives (e.g. masked language modelling for text). Models that have learned to construct cross-modal representations using both modalities are expected to perform worse when inputs are missing from a modality. We find that recently proposed models have much greater relative difficulty predicting text when visual information is ablated, compared to predicting visual object categories when text is ablated, indicating that these models are not symmetrically cross-modal.
HypMix: Hyperbolic Interpolative Data Augmentation
Authors: Ramit Sawhney, Megh Thakkar, Shivam Agarwal, Di Jin, Diyi Yang, Lucie Flek
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Interpolation-based regularisation methods for data augmentation have proven to be effective for various tasks and modalities. These methods involve performing mathematical operations over the raw input samples or their latent states representations - vectors that often possess complex hierarchical geometries. However, these operations are performed in the Euclidean space, simplifying these representations, which may lead to distorted and noisy interpolations. We propose HypMix, a novel model-, data-, and modality-agnostic interpolative data augmentation technique operating in the hyperbolic space, which captures the complex geometry of input and hidden state hierarchies better than its contemporaries. We evaluate HypMix on benchmark and low resource datasets across speech, text, and vision modalities, showing that HypMix consistently outperforms state-of-the-art data augmentation techniques. In addition, we demonstrate the use of HypMix in semi-supervised settings. We further probe into the adversarial robustness and qualitative inferences we draw from HypMix that elucidate the efficacy of the Riemannian hyperbolic manifolds for interpolation-based data augmentation.
Integrating Deep Event-Level and Script-Level Information for Script Event Prediction
Authors: Long Bai, Saiping Guan, Jiafeng Guo, Zixuan Li, Xiaolong Jin, Xueqi Cheng
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Scripts are structured sequences of events together with the participants, which are extracted from the texts. Script event prediction aims to predict the subsequent event given the historical events in the script. Two kinds of information facilitate this task, namely, the event-level information and the script-level information. At the event level, existing studies view an event as a verb with its participants, while neglecting other useful properties, such as the state of the participants. At the script level, most existing studies only consider a single event sequence corresponding to one common protagonist. In this paper, we propose a Transformer-based model, called MCPredictor, which integrates deep event-level and script-level information for script event prediction. At the event level, MCPredictor utilizes the rich information in the text to obtain more comprehensive event semantic representations. At the script-level, it considers multiple event sequences corresponding to different participants of the subsequent event. The experimental results on the widely-used New York Times corpus demonstrate the effectiveness and superiority of the proposed model.
QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions
Authors: Daniela Brook Weiss, Paul Roit, Ayal Klein, Ori Ernst, Ido Dagan
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Multi-text applications, such as multi-document summarization, are typically required to model redundancies across related texts. Current methods confronting consolidation struggle to fuse overlapping information. In order to explicitly represent content overlap, we propose to align predicate-argument relations across texts, providing a potential scaffold for information consolidation. We go beyond clustering coreferring mentions, and instead model overlap with respect to redundancy at a propositional level, rather than merely detecting shared referents. Our setting exploits QA-SRL, utilizing question-answer pairs to capture predicate-argument relations, facilitating laymen annotation of cross-text alignments. We employ crowd-workers for constructing a dataset of QA-based alignments, and present a baseline QA alignment model trained over our dataset. Analyses show that our new task is semantically challenging, capturing content overlap beyond lexical similarity and complements cross-document coreference with proposition-level links, offering potential use for downstream tasks.
PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models
Authors: Torsten Scholak, Nathan Schucher, Dzmitry Bahdanau
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Large pre-trained language models for textual data have an unconstrained output space; at each decoding step, they can produce any of 10,000s of sub-word tokens. When fine-tuned to target constrained formal languages like SQL, these models often generate invalid code, rendering it unusable. We propose PICARD (code available at r̆lhttps://github.com/ElementAI/picard), a method for constraining auto-regressive decoders of language models through incremental parsing. PICARD helps to find valid output sequences by rejecting inadmissible tokens at each decoding step. On the challenging Spider and CoSQL text-to-SQL translation tasks, we show that PICARD transforms fine-tuned T5 models with passable performance into state-of-the-art solutions.
Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings
Authors: Marco Di Giovanni, Marco Brambilla
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Semantic sentence embeddings are usually supervisedly built minimizing distances between pairs of embeddings of sentences labelled as semantically similar by annotators. Since big labelled datasets are rare, in particular for non-English languages, and expensive, recent studies focus on unsupervised approaches that require not-paired input sentences. We instead propose a language-independent approach to build large datasets of pairs of informal texts weakly similar, without manual human effort, exploiting Twitter`s intrinsic powerful signals of relatedness: replies and quotes of tweets. We use the collected pairs to train a Transformer model with triplet-like structures, and we test the generated embeddings on Twitter NLP similarity tasks (PIT and TURL) and STSb. We also introduce four new sentence ranking evaluation benchmarks of informal texts, carefully extracted from the initial collections of tweets, proving not only that our best model learns classical Semantic Textual Similarity, but also excels on tasks where pairs of sentences are not exact paraphrases. Ablation studies reveal how increasing the corpus size influences positively the results, even at 2M samples, suggesting that bigger collections of Tweets still do not contain redundant information about semantic similarities. Code available at r̆lhttps://github.com/marco-digio/Twitter4SSE
Guilt by Association: Emotion Intensities in Lexical Representations
Authors: Shahab Raji, Gerard de Melo
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: What do linguistic models reveal about the emotions associated with words? In this study, we consider the task of estimating word-level emotion intensity scores for specific emotions, exploring unsupervised, supervised, and finally a self-supervised method of extracting emotional associations from pretrained vectors and models. Overall, we find that linguistic models carry substantial potential for inducing fine-grained emotion intensity scores, showing a far higher correlation with human ground truth ratings than state-of-the-art emotion lexicons based on labeled data.
Using Sociolinguistic Variables to Reveal Changing Attitudes Towards Sexuality and Gender
Authors: Sky CH-Wang, David Jurgens
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Individuals signal aspects of their identity and beliefs through linguistic choices. Studying these choices in aggregate allows us to examine large-scale attitude shifts within a population. Here, we develop computational methods to study word choice within a sociolinguistic lexical variable---alternate words used to express the same concept---in order to test for change in the United States towards sexuality and gender. We examine two variables: i) referents to significant others, such as the word “partner” and ii) referents to an indefinite person, both of which could optionally be marked with gender. The linguistic choices in each variable allow us to study increased rates of acceptances of gay marriage and gender equality, respectively. In longitudinal analyses across Twitter and Reddit over 87M messages, we demonstrate that attitudes are changing but that these changes are driven by specific demographics within the United States. Further, in a quasi-causal analysis, we show that passages of Marriage Equality Acts in different states are drivers of linguistic change.
Identifying Morality Frames in Political Tweets using Relational Learning
Authors: Shamik Roy, Maria Leonor Pacheco, Dan Goldwasser
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions. The Moral Foundation Theory identifies five moral foundations, each associated with a positive and negative polarity. However, moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities. In this paper, we introduce morality frames, a representation framework for organizing moral attitudes directed at different entities, and come up with a novel and high-quality annotated dataset of tweets written by US politicians. Then, we propose a relational learning model to predict moral attitudes towards entities and moral foundations jointly. We do qualitative and quantitative evaluations, showing that moral sentiment towards entities differs highly across political ideologies.
Measuring Sentence-Level and Aspect-Level (Un)certainty in Science Communications
Authors: Jiaxin Pei, David Jurgens
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Certainty and uncertainty are fundamental to science communication. Hedges have widely been used as proxies for uncertainty. However, certainty is a complex construct, with authors expressing not only the degree but the type and aspects of uncertainty in order to give the reader a certain impression of what is known. Here, we introduce a new study of certainty that models both the level and the aspects of certainty in scientific findings. Using a new dataset of 2167 annotated scientific findings, we demonstrate that hedges alone account for only a partial explanation of certainty. We show that both the overall certainty and individual aspects can be predicted with pre-trained language models, providing a more complete picture of the author`s intended communication. Downstream analyses on 431K scientific findings from news and scientific abstracts demonstrate that modeling sentence-level and aspect-level certainty is meaningful for areas like science communication. Both the model and datasets used in this paper are released at r̆lhttps://blablablab.si.umich.edu/projects/certainty/.
Assessing the Reliability of Word Embedding Gender Bias Measures
Authors: Yupei Du, Qixiang Fang, Dong Nguyen
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Various measures have been proposed to quantify human-like social biases in word embeddings. However, bias scores based on these measures can suffer from measurement error. One indication of measurement quality is reliability, concerning the extent to which a measure produces consistent results. In this paper, we assess three types of reliability of word embedding gender bias measures, namely test-retest reliability, inter-rater consistency and internal consistency. Specifically, we investigate the consistency of bias scores across different choices of random seeds, scoring rules and words. Furthermore, we analyse the effects of various factors on these measures' reliability scores. Our findings inform better design of word embedding gender bias measures. Moreover, we urge researchers to be more critical about the application of such measures
Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks
Authors: Hongzhan Lin, Jing Ma, Mingfei Cheng, Zhiwei Yang, Liangliang Chen, Guang Chen
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Rumors are rampant in the era of social media. Conversation structures provide valuable clues to differentiate between real and fake claims. However, existing rumor detection methods are either limited to the strict relation of user responses or oversimplify the conversation structure. In this study, to substantially reinforces the interaction of user opinions while alleviating the negative impact imposed by irrelevant posts, we first represent the conversation thread as an undirected interaction graph. We then present a Claim-guided Hierarchical Graph Attention Network for rumor classification, which enhances the representation learning for responsive posts considering the entire social contexts and attends over the posts that can semantically infer the target claim. Extensive experiments on three Twitter datasets demonstrate that our rumor detection method achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.
Learning Bill Similarity with Annotated and Augmented Corpora of Bills
Authors: Jiseon Kim, Elden Griggs, In Song Kim, Alice Oh
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Bill writing is a critical element of representative democracy. However, it is often overlooked that most legislative bills are derived, or even directly copied, from other bills. Despite the significance of bill-to-bill linkages for understanding the legislative process, existing approaches fail to address semantic similarities across bills, let alone reordering or paraphrasing which are prevalent in legal document writing. In this paper, we overcome these limitations by proposing a 5-class classification task that closely reflects the nature of the bill generation process. In doing so, we construct a human-labeled dataset of 4,721 bill-to-bill relationships at the subsection-level and release this annotated dataset to the research community. To augment the dataset, we generate synthetic data with varying degrees of similarity, mimicking the complex bill writing process. We use BERT variants and apply multi-stage training, sequentially fine-tuning our models with synthetic and human-labeled datasets. We find that the predictive performance significantly improves when training with both human-labeled and synthetic data. Finally, we apply our trained model to infer section- and bill-level similarities. Our analysis shows that the proposed methodology successfully captures the similarities across legal documents at various levels of aggregation.
SWEAT: Scoring Polarization of Topics across Different Corpora
Authors: Federico Bianchi, Marco Marelli, Paolo Nicoli, Matteo Palmonari
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Understanding differences of viewpoints across corpora is a fundamental task for computational social sciences. In this paper, we propose the Sliced Word Embedding Association Test (SWEAT), a novel statistical measure to compute the relative polarization of a topical wordset across two distributional representations. To this end, SWEAT uses two additional wordsets, deemed to have opposite valence, to represent two different poles. We validate our approach and illustrate a case study to show the usefulness of the introduced measure.
“So You Think You`re Funny?”: Rating the Humour Quotient in Standup Comedy
Authors: Anirudh Mittal, Pranav Jeevan P, Prerak Gandhi, Diptesh Kanojia, Pushpak Bhattacharyya
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Computational Humour (CH) has attracted the interest of Natural Language Processing and Computational Linguistics communities. Creating datasets for automatic measurement of humour quotient is difficult due to multiple possible interpretations of the content. In this work, we create a multi-modal humour-annotated dataset (~40 hours) using stand-up comedy clips. We devise a novel scoring mechanism to annotate the training data with a humour quotient score using the audience`s laughter. The normalized duration (laughter duration divided by the clip duration) of laughter in each clip is used to compute this humour coefficient score on a five-point scale (0-4). This method of scoring is validated by comparing with manually annotated scores, wherein a quadratic weighted kappa of 0.6 is obtained. We use this dataset to train a model that provides a \textquoteleftfunniness' score, on a five-point scale, given the audio and its corresponding text. We compare various neural language models for the task of humour-rating and achieve an accuracy of 0.813 in terms of Quadratic Weighted Kappa (QWK). Our \textquoteleftOpen Mic' dataset is released for further research along with the code.
“Was it “stated” or was it “claimed”?: How linguistic bias affects generative language models
Authors: Roma Patel, Ellie Pavlick
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: People use language in subtle and nuanced ways to convey their beliefs. For instance, saying \textitclaimed instead of \textitsaid casts doubt on the truthfulness of the underlying proposition, thus representing the author`s opinion on the matter. Several works have identified such linguistic classes of words that occur frequently in natural language text and are bias-inducing by virtue of their framing effects. In this paper, we test whether generative language models (including GPT-2 (CITATION) are sensitive to these linguistic framing effects. In particular, we test whether prompts that contain linguistic markers of author bias (e.g., hedges, implicatives, subjective intensifiers, assertives) influence the distribution of the generated text. Although these framing effects are subtle and stylistic, we find evidence that they lead to measurable style and topic differences in the generated text, leading to language that is, on average, more polarised and more skewed towards controversial entities and events.
PAUSE: Positive and Annealed Unlabeled Sentence Embedding
Authors: Lele Cao, Emil Larsson, Vilhelm von Ehrenheim, Dhiana Deva Cavalcanti Rocha, Anna Martin, Sonja Horn
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Sentence embedding refers to a set of effective and versatile techniques for converting raw text into numerical vector representations that can be used in a wide range of natural language processing (NLP) applications. The majority of these techniques are either supervised or unsupervised. Compared to the unsupervised methods, the supervised ones make less assumptions about optimization objectives and usually achieve better results. However, the training requires a large amount of labeled sentence pairs, which is not available in many industrial scenarios. To that end, we propose a generic and end-to-end approach -- PAUSE (Positive and Annealed Unlabeled Sentence Embedding), capable of learning high-quality sentence embeddings from a partially labeled dataset. We experimentally show that PAUSE achieves, and sometimes surpasses, state-of-the-art results using only a small fraction of labeled sentence pairs on various benchmark tasks. When applied to a real industrial use case where labeled samples are scarce, PAUSE encourages us to extend our dataset without the burden of extensive manual annotation work.
A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders
Authors: Maarten De Raedt, Fréderic Godin, Pieter Buteneers, Chris Develder, Thomas Demeester
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Powerful sentence encoders trained for multiple languages are on the rise. These systems are capable of embedding a wide range of linguistic properties into vector representations. While explicit probing tasks can be used to verify the presence of specific linguistic properties, it is unclear whether the vector representations can be manipulated to indirectly steer such properties. For efficient learning, we investigate the use of a geometric mapping in embedding space to transform linguistic properties, without any tuning of the pre-trained sentence encoder or decoder. We validate our approach on three linguistic properties using a pre-trained multilingual autoencoder and analyze the results in both monolingual and cross-lingual settings.
An Information-Theoretic Characterization of Morphological Fusion
Authors: Neil Rathi, Michael Hahn, Richard Futrell
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Linguistic typology generally divides synthetic languages into groups based on their morphological fusion. However, this measure has long been thought to be best considered a matter of degree. We present an information-theoretic measure, called informational fusion, to quantify the degree of fusion of a given set of morphological features in a surface form, which naturally provides such a graded scale. Informational fusion is able to encapsulate not only concatenative, but also nonconcatenative morphological systems (e.g. Arabic), abstracting away from any notions of morpheme segmentation. We then show, on a sample of twenty-one languages, that our measure recapitulates the usual linguistic classifications for concatenative systems, and provides new measures for nonconcatenative ones. We also evaluate the long-standing hypotheses that more frequent forms are more fusional, and that paradigm size anticorrelates with degree of fusion. We do not find evidence for the idea that languages have characteristic levels of fusion; rather, the degree of fusion varies across part-of-speech within languages.
The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning
Authors: Yuchen Lian, Arianna Bisazza, Tessa Verhoef
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Natural languages display a trade-off among different strategies to convey syntactic structure, such as word order or inflection. This trade-off, however, has not appeared in recent simulations of iterated language learning with neural network agents (Chaabouni et al., 2019b). We re-evaluate this result in light of three factors that play an important role in comparable experiments from the Language Evolution field: (i) speaker bias towards efficient messaging, (ii) non systematic input languages, and (iii) learning bottleneck. Our simulations show that neural agents mainly strive to maintain the utterance type distribution observed during learning, instead of developing a more efficient or systematic language.
On Classifying whether Two Texts are on the Same Side of an Argument
Authors: Erik Körner, Gregor Wiedemann, Ahmad Dawar Hakimi, Gerhard Heyer, Martin Potthast
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: To ease the difficulty of argument stance classification, the task of same side stance classification (S3C) has been proposed. In contrast to actual stance classification, which requires a substantial amount of domain knowledge to identify whether an argument is in favor or against a certain issue, it is argued that, for S3C, only argument similarity within stances needs to be learned to successfully solve the task. We evaluate several transformer-based approaches on the dataset of the recent S3C shared task, followed by an in-depth evaluation and error analysis of our model and the task`s hypothesis. We show that, although we achieve state-of-the-art results, our model fails to generalize both within as well as across topics and domains when adjusting the sampling strategy of the training and test set to a more adversarial scenario. Our evaluation shows that current state-of-the-art approaches cannot determine same side stance by considering only domain-independent linguistic similarity features, but appear to require domain knowledge and semantic inference, too.
Chinese Opinion Role Labeling with Corpus Translation: A Pivot Study
Authors: Ranran Zhen, Rui Wang, Guohong Fu, Chengguo Lv, Meishan Zhang
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Opinion Role Labeling (ORL), aiming to identify the key roles of opinion, has received increasing interest. Unlike most of the previous works focusing on the English language, in this paper, we present the first work of Chinese ORL. We construct a Chinese dataset by manually translating and projecting annotations from a standard English MPQA dataset. Then, we investigate the effectiveness of cross-lingual transfer methods, including model transfer and corpus translation. We exploit multilingual BERT with Contextual Parameter Generator and Adapter methods to examine the potentials of unsupervised cross-lingual learning and our experiments and analyses for both bilingual and multilingual transfers establish a foundation for the future research of this task.
MassiveSumm: a very large-scale, very multilingual, news summarisation dataset
Authors: Daniel Varab, Natalie Schluter
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Current research in automatic summarisation is unapologetically anglo-centered--a persistent state-of-affairs, which also predates neural net approaches. High-quality automatic summarisation datasets are notoriously expensive to create, posing a challenge for any language. However, with digitalisation, archiving, and social media advertising of newswire articles, recent work has shown how, with careful methodology application, large-scale datasets can now be simply gathered instead of written. In this paper, we present a large-scale multilingual summarisation dataset containing articles in 92 languages, spread across 28.8 million articles, in more than 35 writing scripts. This is both the largest, most inclusive, existing automatic summarisation dataset, as well as one of the largest, most inclusive, ever published datasets for any NLP task. We present the first investigation on the efficacy of resource building from news platforms in the low-resource language setting. Finally, we provide some first insight on how low-resource language settings impact state-of-the-art automatic summarisation system performance.
AUTOSUMM: Automatic Model Creation for Text Summarization
Authors: Sharmila Reddy Nangi, Atharv Tyagi, Jay Mundra, Sagnik Mukherjee, Raj Snehal, Niyati Chhaya, Aparna Garimella
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Recent efforts to develop deep learning models for text generation tasks such as extractive and abstractive summarization have resulted in state-of-the-art performances on various datasets. However, obtaining the best model configuration for a given dataset requires an extensive knowledge of deep learning specifics like model architecture, tuning parameters etc., and is often extremely challenging for a non-expert. In this paper, we propose methods to automatically create deep learning models for the tasks of extractive and abstractive text summarization. Based on the recent advances in Automated Machine Learning and the success of large language models such as BERT and GPT-2 in encoding knowledge, we use a combination of Neural Architecture Search (NAS) and Knowledge Distillation (KD) techniques to perform model search and compression using the vast knowledge provided by these language models to develop smaller, customized models for any given dataset. We present extensive empirical results to illustrate the effectiveness of our model creation methods in terms of inference time and model size, while achieving near state-of-the-art performances in terms of accuracy across a range of datasets.
Investigating the Helpfulness of Word-Level Quality Estimation for Post-Editing Machine Translation Output
Authors: Raksha Shenoy, Nico Herbig, Antonio Krüger, Josef van Genabith
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Compared to fully manual translation, post-editing (PE) machine translation (MT) output can save time and reduce errors. Automatic word-level quality estimation (QE) aims to predict the correctness of words in MT output and holds great promise to aid PE by flagging problematic output. Quality of QE is crucial, as incorrect QE might lead to translators missing errors or wasting time on already correct MT output. Achieving accurate automatic word-level QE is very hard, and it is currently not known (i) at what quality threshold QE is actually beginning to be useful for human PE, and (ii), how to best present word-level QE information to translators. In particular, should word-level QE visualization indicate uncertainty of the QE model or not? In this paper, we address both research questions with real and simulated word-level QE, visualizations, and user studies, where time, subjective ratings, and quality of the final translations are assessed. Results show that current word-level QE models are not yet good enough to support PE. Instead, quality levels of \ensuremath> 80% F1 are required. For helpful quality levels, a visualization reflecting the uncertainty of the QE model is preferred. Our analysis further shows that speed gains achieved through QE are not merely a result of blindly trusting the QE system, but that the quality of the final translations also improves. The threshold results from the paper establish a quality goal for future word-level QE research.
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts
Authors: Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, Sebastian Ruder
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Massively multilingual language models such as multilingual BERT offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. However, due to limited capacity and large differences in pretraining data sizes, there is a profound performance gap between resource-rich and resource-poor target languages. The ultimate challenge is dealing with under-resourced languages not covered at all by the models and written in scripts unseen during pretraining. In this work, we propose a series of novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts. Relying on matrix factorization, our methods capitalize on the existing latent knowledge about multiple languages already available in the pretrained model`s embedding matrix. Furthermore, we show that learning of the new dedicated embedding matrix in the target language can be improved by leveraging a small number of vocabulary items (i.e., the so-called lexically overlapping tokens) shared between mBERT`s and target language vocabulary. Our adaptation techniques offer substantial performance gains for languages with unseen scripts. We also demonstrate that they can yield improvements for low-resource languages written in scripts covered by the pretrained model.
Neural Machine Translation Quality and Post-Editing Performance
Authors: Vilém Zouhar, Martin Popel, Ondřej Bojar, Aleš Tamchyna
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: We test the natural expectation that using MT in professional translation saves human processing time. The last such study was carried out by Sanchez-Torron and Koehn (2016) with phrase-based MT, artificially reducing the translation quality. In contrast, we focus on neural MT (NMT) of high quality, which has become the state-of-the-art approach since then and also got adopted by most translation companies. Through an experimental study involving over 30 professional translators for English -\ensuremath> Czech translation, we examine the relationship between NMT performance and post-editing time and quality. Across all models, we found that better MT systems indeed lead to fewer changes in the sentences in this industry setting. The relation between system quality and post-editing time is however not straightforward and, contrary to the results on phrase-based MT, BLEU is definitely not a stable predictor of the time or final output quality.
XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation
Authors: Sebastian Ruder, Noah Constant, Jan Botha, Aditya Siddhant, Orhan Firat, Jinlan Fu, Pengfei Liu, Junjie Hu, Dan Garrette, Graham Neubig, Melvin Johnson
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Machine learning has brought striking advances in multilingual natural language processing capabilities over the past year. For example, the latest techniques have improved the state-of-the-art performance on the XTREME multilingual benchmark by more than 13 points. While a sizeable gap to human-level performance remains, improvements have been easier to achieve in some tasks than in others. This paper analyzes the current state of cross-lingual transfer learning and summarizes some lessons learned. In order to catalyze meaningful progress, we extend XTREME to XTREME-R, which consists of an improved set of ten natural language understanding tasks, including challenging language-agnostic retrieval tasks, and covers 50 typologically diverse languages. In addition, we provide a massively multilingual diagnostic suite and fine-grained multi-dataset evaluation capabilities through an interactive public leaderboard to gain a better understanding of such models.
Contrastive Conditioning for Assessing Disambiguation in MT: A Case Study of Distilled Bias
Authors: Jannis Vamvas, Rico Sennrich
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Lexical disambiguation is a major challenge for machine translation systems, especially if some senses of a word are trained less often than others. Identifying patterns of overgeneralization requires evaluation methods that are both reliable and scalable. We propose contrastive conditioning as a reference-free black-box method for detecting disambiguation errors. Specifically, we score the quality of a translation by conditioning on variants of the source that provide contrastive disambiguation cues. After validating our method, we apply it in a case study to perform a targeted evaluation of sequence-level knowledge distillation. By probing word sense disambiguation and translation of gendered occupation names, we show that distillation-trained models tend to overgeneralize more than other models with a comparable BLEU score. Contrastive conditioning thus highlights a side effect of distillation that is not fully captured by standard evaluation metrics. Code and data to reproduce our findings are publicly available.