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ACL | WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types | Multimodal Entity Linking (MEL) which aims at linking mentions with multimodal contexts to the referent entities from a knowledge base (e.g., Wikipedia), is an essential task for many multimodal applications. Although much attention has been paid to MEL, the shortcomings of existing MEL datasets including limited conte... | 383097b0e600f07361a89a7e6d4e8388 | 2,022 | [
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ACL | Dynamically Adjusting Transformer Batch Size by Monitoring Gradient Direction Change | The choice of hyper-parameters affects the performance of neural models. While much previous research (Sutskever et al., 2013; Duchi et al., 2011; Kingma and Ba, 2015) focuses on accelerating convergence and reducing the effects of the learning rate, comparatively few papers concentrate on the effect of batch size. In ... | 16dfc6c1c1a7634f97d4afdbe268fdc3 | 2,020 | [
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ACL | NAT: Noise-Aware Training for Robust Neural Sequence Labeling | Sequence labeling systems should perform reliably not only under ideal conditions but also with corrupted inputs—as these systems often process user-generated text or follow an error-prone upstream component. To this end, we formulate the noisy sequence labeling problem, where the input may undergo an unknown noising p... | 68ccedb1f6ee4e5c3dd1d868b9809ab0 | 2,020 | [
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ACL | Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order | Masked language model and autoregressive language model are two types of language models. While pretrained masked language models such as BERT overwhelm the line of natural language understanding (NLU) tasks, autoregressive language models such as GPT are especially capable in natural language generation (NLG). In this... | d36e2d099c171191b7e9167e038715a7 | 2,020 | [
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ACL | Crafting Adversarial Examples for Neural Machine Translation | Effective adversary generation for neural machine translation (NMT) is a crucial prerequisite for building robust machine translation systems. In this work, we investigate veritable evaluations of NMT adversarial attacks, and propose a novel method to craft NMT adversarial examples. We first show the current NMT advers... | c7667eafe673c934617be12716625aff | 2,021 | [
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ACL | Effective Adversarial Regularization for Neural Machine Translation | A regularization technique based on adversarial perturbation, which was initially developed in the field of image processing, has been successfully applied to text classification tasks and has yielded attractive improvements. We aim to further leverage this promising methodology into more sophisticated and critical neu... | 33d348921f761b16fbb7d814094a6694 | 2,019 | [
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ACL | Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives | This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens. We propose a curriculum learning (CL) based Pointer-Generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alter... | 472080a59ea4d63b14f22972c1fb5839 | 2,019 | [
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ACL | Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction | Effective projection-based cross-lingual word embedding (CLWE) induction critically relies on the iterative self-learning procedure. It gradually expands the initial small seed dictionary to learn improved cross-lingual mappings. In this work, we present ClassyMap, a classification-based approach to self-learning, yiel... | bb16dc9ae797a300aad1d66bb5a093b6 | 2,020 | [
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ACL | Rigid Formats Controlled Text Generation | Neural text generation has made tremendous progress in various tasks. One common characteristic of most of the tasks is that the texts are not restricted to some rigid formats when generating. However, we may confront some special text paradigms such as Lyrics (assume the music score is given), Sonnet, SongCi (classica... | d28014812835f79c597a979e428a6f1c | 2,020 | [
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ACL | Like a Baby: Visually Situated Neural Language Acquisition | We examine the benefits of visual context in training neural language models to perform next-word prediction. A multi-modal neural architecture is introduced that outperform its equivalent trained on language alone with a 2% decrease in perplexity, even when no visual context is available at test. Fine-tuning the embed... | ea1923d7b28acc0777e013e2d127a04b | 2,019 | [
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ACL | STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework | Simultaneous translation, which translates sentences before they are finished, is use- ful in many scenarios but is notoriously dif- ficult due to word-order differences. While the conventional seq-to-seq framework is only suitable for full-sentence translation, we pro- pose a novel prefix-to-prefix framework for si- m... | d307298a82be037eef02ef9a22f85d34 | 2,019 | [
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ACL | A Cluster-based Approach for Improving Isotropy in Contextual Embedding Space | The representation degeneration problem in Contextual Word Representations (CWRs) hurts the expressiveness of the embedding space by forming an anisotropic cone where even unrelated words have excessively positive correlations. Existing techniques for tackling this issue require a learning process to re-train models wi... | 427fcc45b02b3ee6ff43781094988df0 | 2,021 | [
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ACL | Addressing Posterior Collapse with Mutual Information for Improved Variational Neural Machine Translation | This paper proposes a simple and effective approach to address the problem of posterior collapse in conditional variational autoencoders (CVAEs). It thus improves performance of machine translation models that use noisy or monolingual data, as well as in conventional settings. Extending Transformer and conditional VAEs... | cb2f7f1b9e7ea37106cdb9464f3628c8 | 2,020 | [
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ACL | DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction | Pattern-based labeling methods have achieved promising results in alleviating the inevitable labeling noises of distantly supervised neural relation extraction. However, these methods require significant expert labor to write relation-specific patterns, which makes them too sophisticated to generalize quickly. To ease ... | b82980a30c8bc3835e4d1b7c61697aaa | 2,019 | [
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ACL | MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER | Named Entity Recognition (NER) for low-resource languages is a both practical and challenging research problem. This paper addresses zero-shot transfer for cross-lingual NER, especially when the amount of source-language training data is also limited. The paper first proposes a simple but effective labeled sequence tra... | 050f66a3d93d08120d100a8d2f3da08b | 2,021 | [
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ACL | Simple and Effective Paraphrastic Similarity from Parallel Translations | We present a model and methodology for learning paraphrastic sentence embeddings directly from bitext, removing the time-consuming intermediate step of creating para-phrase corpora. Further, we show that the resulting model can be applied to cross lingual tasks where it both outperforms and is orders of magnitude faste... | 283606159ff2a132386236480c962948 | 2,019 | [
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ACL | A Conditional Splitting Framework for Efficient Constituency Parsing | We introduce a generic seq2seq parsing framework that casts constituency parsing problems (syntactic and discourse parsing) into a series of conditional splitting decisions. Our parsing model estimates the conditional probability distribution of possible splitting points in a given text span and supports efficient top-... | b9b3acd3fbb710c20860897f784a1be9 | 2,021 | [
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ACL | ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection | Toxic language detection systems often falsely flag text that contains minority group mentions as toxic, as those groups are often the targets of online hate. Such over-reliance on spurious correlations also causes systems to struggle with detecting implicitly toxic language.To help mitigate these issues, we create Tox... | 4c605516a8ec1fd719cb30a51b85a5e2 | 2,022 | [
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ACL | An Improved Model for Voicing Silent Speech | In this paper, we present an improved model for voicing silent speech, where audio is synthesized from facial electromyography (EMG) signals. To give our model greater flexibility to learn its own input features, we directly use EMG signals as input in the place of hand-designed features used by prior work. Our model u... | d9493b0d543109c79fa0d4b0f1f56987 | 2,021 | [
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ACL | Predicting Sentence Deletions for Text Simplification Using a Functional Discourse Structure | Document-level text simplification often deletes some sentences besides performing lexical, grammatical or structural simplification to reduce text complexity. In this work, we focus on sentence deletions for text simplification and use a news genre-specific functional discourse structure, which categorizes sentences b... | 2cc0db7fd41ac532e9e6884d46da715c | 2,022 | [
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ACL | Named Entity Recognition as Dependency Parsing | Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. NER research is often focused on flat entities only (flat NER), ignoring the fact that entity references can be nested, as in [Bank of [China]] (Finkel and Man... | 260153d02de8dcd8cc22431259b4a3f6 | 2,020 | [
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ACL | Lattice Transformer for Speech Translation | Recent advances in sequence modeling have highlighted the strengths of the transformer architecture, especially in achieving state-of-the-art machine translation results. However, depending on the up-stream systems, e.g., speech recognition, or word segmentation, the input to translation system can vary greatly. The go... | 8d6cddab1624c1009a6127f83352abc6 | 2,019 | [
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ACL | XQA: A Cross-lingual Open-domain Question Answering Dataset | Open-domain question answering (OpenQA) aims to answer questions through text retrieval and reading comprehension. Recently, lots of neural network-based models have been proposed and achieved promising results in OpenQA. However, the success of these models relies on a massive volume of training data (usually in Engli... | 64243601ec2b0c6effb2f62a08dc3505 | 2,019 | [
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ACL | Few-NERD: A Few-shot Named Entity Recognition Dataset | Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark data specifically focused on the practical and challenging task. Current approaches collect existing supervised NER datasets and re-organize them to the few-shot setting for empiric... | 2486f6b7f6b9921fca70aad86689896a | 2,021 | [
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ACL | Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation | Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge of source domain to the unlabeled target domain. Existing methods typically require to learn to adapt the target model by exploiting the source data and sharing the network architecture across domains. However, this pipeline makes the source data risky... | 775db9e3af934e5547164e0862c79402 | 2,021 | [
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ACL | Prefix-Tuning: Optimizing Continuous Prompts for Generation | Fine-tuning is the de facto way of leveraging large pretrained language models for downstream tasks. However, fine-tuning modifies all the language model parameters and therefore necessitates storing a full copy for each task. In this paper, we propose prefix-tuning, a lightweight alternative to fine-tuning for natural... | 05acea6d50e698d56677579a7efb5e93 | 2,021 | [
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ACL | Multi-Task Learning for Coherence Modeling | We address the task of assessing discourse coherence, an aspect of text quality that is essential for many NLP tasks, such as summarization and language assessment. We propose a hierarchical neural network trained in a multi-task fashion that learns to predict a document-level coherence score (at the network’s top laye... | e033a330cc63fe5e3ad92d13fd1822d2 | 2,019 | [
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ACL | Towards Abstractive Grounded Summarization of Podcast Transcripts | Podcasts have shown a recent rise in popularity. Summarization of podcasts is of practical benefit to both content providers and consumers. It helps people quickly decide whether they will listen to a podcast and/or reduces the cognitive load of content providers to write summaries. Nevertheless, podcast summarization ... | 4771f066c643a4691be056769c09f355 | 2,022 | [
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ACL | What does the sea say to the shore? A BERT based DST style approach for speaker to dialogue attribution in novels | We present a complete pipeline to extract characters in a novel and link them to their direct-speech utterances. Our model is divided into three independent components: extracting direct-speech, compiling a list of characters, and attributing those characters to their utterances. Although we find that existing systems ... | df2fb32f3406923d7dfd72520b47393c | 2,022 | [
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ACL | Kronecker Decomposition for GPT Compression | GPT is an auto-regressive Transformer-based pre-trained language model which has attracted a lot of attention in the natural language processing (NLP) domain. The success of GPT is mostly attributed to its pre-training on huge amount of data and its large number of parameters. Despite the superior performance of GPT, t... | 19a4628b5ca116ed1ce8df901c25afad | 2,022 | [
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ACL | Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder | Capturing interactions among event arguments is an essential step towards robust event argument extraction (EAE). However, existing efforts in this direction suffer from two limitations: 1) The argument role type information of contextual entities is mainly utilized as training signals, ignoring the potential merits of... | 91ea9532ba44fe09602262326d60b443 | 2,021 | [
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ACL | Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data | A number of researchers have recently questioned the necessity of increasingly complex neural network (NN) architectures. In particular, several recent papers have shown that simpler, properly tuned models are at least competitive across several NLP tasks. In this work, we show that this is also the case for text gener... | 74835f96956479e8eb6ce24261459f4f | 2,020 | [
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ACL | A Mixture of h - 1 Heads is Better than h Heads | Multi-head attentive neural architectures have achieved state-of-the-art results on a variety of natural language processing tasks. Evidence has shown that they are overparameterized; attention heads can be pruned without significant performance loss. In this work, we instead “reallocate” them—the model learns to activ... | e6b47bc40de093b30587938974c9b040 | 2,020 | [
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ACL | CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech | Although there is an unprecedented effort to provide adequate responses in terms of laws and policies to hate content on social media platforms, dealing with hatred online is still a tough problem. Tackling hate speech in the standard way of content deletion or user suspension may be charged with censorship and overblo... | cc99d5c7f8b8a2691fee9d29fe125f1f | 2,019 | [
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ACL | How to Best Use Syntax in Semantic Role Labelling | There are many different ways in which external information might be used in a NLP task. This paper investigates how external syntactic information can be used most effectively in the Semantic Role Labeling (SRL) task. We evaluate three different ways of encoding syntactic parses and three different ways of injecting t... | 874a58cd757766f9decd49cc3d0bb21f | 2,019 | [
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ACL | Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader | We propose a new end-to-end question answering model, which learns to aggregate answer evidence from an incomplete knowledge base (KB) and a set of retrieved text snippets.Under the assumptions that structured data is easier to query and the acquired knowledge can help the understanding of unstructured text, our model ... | a30ad7daf90d48182377e170fd887490 | 2,019 | [
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ACL | DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations | Emotion Recognition in Conversations (ERC) has gained increasing attention for developing empathetic machines. Recently, many approaches have been devoted to perceiving conversational context by deep learning models. However, these approaches are insufficient in understanding the context due to lacking the ability to e... | ca1a72babe4e729d9a8e596354925d6d | 2,021 | [
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ACL | Encouraging Paragraph Embeddings to Remember Sentence Identity Improves Classification | While paragraph embedding models are remarkably effective for downstream classification tasks, what they learn and encode into a single vector remains opaque. In this paper, we investigate a state-of-the-art paragraph embedding method proposed by Zhang et al. (2017) and discover that it cannot reliably tell whether a g... | 10b7a968b9f4290b35056131cc7f9d57 | 2,019 | [
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ACL | Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization | Text style transfer aims to alter the style (e.g., sentiment) of a sentence while preserving its content. A common approach is to map a given sentence to content representation that is free of style, and the content representation is fed to a decoder with a target style. Previous methods in filtering style completely r... | c3ba83489bcd964f70272c16dab27545 | 2,021 | [
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ACL | Predicting the Focus of Negation: Model and Error Analysis | The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances. In this paper, we experiment with neural networks to predict the focus of negation. Our main novelty is leveraging a scope detector to introduce the scope of negation as... | 34d53ede3b64c0ad0ad7fc6ca4a1b541 | 2,020 | [
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ACL | Unsupervised Question Answering by Cloze Translation | Obtaining training data for Question Answering (QA) is time-consuming and resource-intensive, and existing QA datasets are only available for limited domains and languages. In this work, we explore to what extent high quality training data is actually required for Extractive QA, and investigate the possibility of unsup... | c683eb290e89d8cff1d4f398ee45679f | 2,019 | [
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ACL | Language Modeling with Shared Grammar | Sequential recurrent neural networks have achieved superior performance on language modeling, but overlook the structure information in natural language. Recent works on structure-aware models have shown promising results on language modeling. However, how to incorporate structure knowledge on corpus without syntactic ... | 6774dd1a897d5d48cf46d91b12a3b22b | 2,019 | [
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ACL | How Do Seq2Seq Models Perform on End-to-End Data-to-Text Generation? | With the rapid development of deep learning, Seq2Seq paradigm has become prevalent for end-to-end data-to-text generation, and the BLEU scores have been increasing in recent years. However, it is widely recognized that there is still a gap between the quality of the texts generated by models and the texts written by hu... | 45346910869263c09eb48a62be501e0b | 2,022 | [
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ACL | Single Model Ensemble using Pseudo-Tags and Distinct Vectors | Model ensemble techniques often increase task performance in neural networks; however, they require increased time, memory, and management effort. In this study, we propose a novel method that replicates the effects of a model ensemble with a single model. Our approach creates K-virtual models within a single parameter... | 8e9b7f5b32456027c16df80df0c5aa99 | 2,020 | [
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ACL | Toward Gender-Inclusive Coreference Resolution | Correctly resolving textual mentions of people fundamentally entails making inferences about those people. Such inferences raise the risk of systemic biases in coreference resolution systems, including biases that can harm binary and non-binary trans and cis stakeholders. To better understand such biases, we foreground... | 1560aa560511991414c34a591f34ac03 | 2,020 | [
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ACL | Gender Gap in Natural Language Processing Research: Disparities in Authorship and Citations | Disparities in authorship and citations across gender can have substantial adverse consequences not just on the disadvantaged genders, but also on the field of study as a whole. Measuring gender gaps is a crucial step towards addressing them. In this work, we examine female first author percentages and the citations to... | 9db7e2a9933c358d60d3774fcad17bd2 | 2,020 | [
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ACL | Choosing Transfer Languages for Cross-Lingual Learning | Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on low-resource languages. However, given a particular task language, it is not clear which language t... | 9010cb6b507065172ce7c7f082717522 | 2,019 | [
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ACL | Towards Better Characterization of Paraphrases | To effectively characterize the nature of paraphrase pairs without expert human annotation, we proposes two new metrics: word position deviation (WPD) and lexical deviation (LD). WPD measures the degree of structural alteration, while LD measures the difference in vocabulary used. We apply these metrics to better under... | 80702cd3fff8b4243807cb6a9fb2836e | 2,022 | [
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ACL | AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages | Pretrained multilingual models are able to perform cross-lingual transfer in a zero-shot setting, even for languages unseen during pretraining. However, prior work evaluating performance on unseen languages has largely been limited to low-level, syntactic tasks, and it remains unclear if zero-shot learning of high-leve... | a850b1c98bbc81f3cb09fc75c995d1b5 | 2,022 | [
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ACL | Language (Technology) is Power: A Critical Survey of “Bias” in NLP | We survey 146 papers analyzing “bias” in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing “bias” is an inherently normative process. We further find that these papers’ proposed quantitative techniques for measuring or mitigati... | 6a5f261ed03d253fb06a03f40b72ce1d | 2,020 | [
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ACL | tBERT: Topic Models and BERT Joining Forces for Semantic Similarity Detection | Semantic similarity detection is a fundamental task in natural language understanding. Adding topic information has been useful for previous feature-engineered semantic similarity models as well as neural models for other tasks. There is currently no standard way of combining topics with pretrained contextual represent... | fb4fa27d9a271448503e65b0a5270fea | 2,020 | [
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ACL | Knowing the No-match: Entity Alignment with Dangling Cases | This paper studies a new problem setting of entity alignment for knowledge graphs (KGs). Since KGs possess different sets of entities, there could be entities that cannot find alignment across them, leading to the problem of dangling entities. As the first attempt to this problem, we construct a new dataset and design ... | b5924eef8060ea769a2dba41ebb87d85 | 2,021 | [
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ACL | Semi-supervised Domain Adaptation for Dependency Parsing with Dynamic Matching Network | Supervised parsing models have achieved impressive results on in-domain texts. However, their performances drop drastically on out-of-domain texts due to the data distribution shift. The shared-private model has shown its promising advantages for alleviating this problem via feature separation, whereas prior works pay ... | 32da8b03968f552af330b9bc4fe34a7f | 2,022 | [
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ACL | Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies | Organisations disclose their privacy practices by posting privacy policies on their websites. Even though internet users often care about their digital privacy, they usually do not read privacy policies, since understanding them requires a significant investment of time and effort. Natural language processing has been ... | 4bdc872131cd35ba0c192da0fff8b457 | 2,021 | [
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ACL | SciREX: A Challenge Dataset for Document-Level Information Extraction | Extracting information from full documents is an important problem in many domains, but most previous work focus on identifying relationships within a sentence or a paragraph. It is challenging to create a large-scale information extraction (IE) dataset at the document level since it requires an understanding of the wh... | e16832937a81fed3081fda40e2fd3832 | 2,020 | [
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ACL | Nested Named Entity Recognition with Span-level Graphs | Span-based methods with the neural networks backbone have great potential for the nested named entity recognition (NER) problem. However, they face problems such as degenerating when positive instances and negative instances largely overlap. Besides, the generalization ability matters a lot in nested NER, as a large pr... | a8675eacdd18afedac6a5c7ee31bdf16 | 2,022 | [
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ACL | Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper) | Sarcasm is often expressed through several verbal and non-verbal cues, e.g., a change of tone, overemphasis in a word, a drawn-out syllable, or a straight looking face. Most of the recent work in sarcasm detection has been carried out on textual data. In this paper, we argue that incorporating multimodal cues can impro... | d7cc8ac080ab5005797d0a085194d8ef | 2,019 | [
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ACL | Enhancing Air Quality Prediction with Social Media and Natural Language Processing | Accompanied by modern industrial developments, air pollution has already become a major concern for human health. Hence, air quality measures, such as the concentration of PM2.5, have attracted increasing attention. Even some studies apply historical measurements into air quality forecast, the changes of air quality co... | 7e3ab6b4d3968cc9f1d0c2943ffe4f98 | 2,019 | [
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ACL | Men Are Elected, Women Are Married: Events Gender Bias on Wikipedia | Human activities can be seen as sequences of events, which are crucial to understanding societies. Disproportional event distribution for different demographic groups can manifest and amplify social stereotypes, and potentially jeopardize the ability of members in some groups to pursue certain goals. In this paper, we ... | 0a5983b88a26e30a6ade9770ef87db68 | 2,021 | [
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ACL | HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization | Document structure is critical for efficient information consumption. However, it is challenging to encode it efficiently into the modern Transformer architecture. In this work, we present HIBRIDS, which injects Hierarchical Biases foR Incorporating Document Structure into attention score calculation. We further presen... | 767d7e4a0d622bb737efb6aaf0be3230 | 2,022 | [
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ACL | Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition | Training Transformer-based models demands a large amount of data, while obtaining aligned and labelled data in multimodality is rather cost-demanding, especially for audio-visual speech recognition (AVSR). Thus it makes a lot of sense to make use of unlabelled unimodal data. On the other side, although the effectivenes... | 16bfd1e87e5d802633e93d9b950bf85d | 2,022 | [
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ACL | ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction | Natural reading orders of words are crucial for information extraction from form-like documents. Despite recent advances in Graph Convolutional Networks (GCNs) on modeling spatial layout patterns of documents, they have limited ability to capture reading orders of given word-level node representations in a graph. We pr... | 18594d79fbf771f181165b6f0199e3ab | 2,021 | [
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ACL | Language (Re)modelling: Towards Embodied Language Understanding | While natural language understanding (NLU) is advancing rapidly, today’s technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization. This work proposes an approach to representation and learning based on the tenets of embodied ... | 61a9cbe4adb8e6172d8fcaffe2f8a7c2 | 2,020 | [
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ACL | Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering | One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis. However, existing approaches do not explicitly train QA models on how to resolve the dependency, and thus these models are limited in understanding human dialogues. In this p... | f338ccd0daf090d147f90c0936ac953a | 2,021 | [
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ACL | Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation | Implicit knowledge, such as common sense, is key to fluid human conversations. Current neural response generation (RG) models are trained to generate responses directly, omitting unstated implicit knowledge. In this paper, we present Think-Before-Speaking (TBS), a generative approach to first externalize implicit commo... | f2c1bd4f0841c1a22bbd71f558ac7b6e | 2,022 | [
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ACL | Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog | Recent studies have shown remarkable success in end-to-end task-oriented dialog system. However, most neural models rely on large training data, which are only available for a certain number of task domains, such as navigation and scheduling. This makes it difficult to scalable for a new domain with limited labeled dat... | 26a70349c36587120bc9e4e1d4775cec | 2,020 | [
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ACL | A Comparison of Strategies for Source-Free Domain Adaptation | Data sharing restrictions are common in NLP, especially in the clinical domain, but there is limited research on adapting models to new domains without access to the original training data, a setting known as source-free domain adaptation. We take algorithms that traditionally assume access to the source-domain trainin... | ed555872e0f7e2a555a31dcbd2137409 | 2,022 | [
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ACL | Modeling Hierarchical Syntax Structure with Triplet Position for Source Code Summarization | Automatic code summarization, which aims to describe the source code in natural language, has become an essential task in software maintenance. Our fellow researchers have attempted to achieve such a purpose through various machine learning-based approaches. One key challenge keeping these approaches from being practic... | 772c63eac917154825b6191a87849e95 | 2,022 | [
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ACL | Miss Tools and Mr Fruit: Emergent Communication in Agents Learning about Object Affordances | Recent research studies communication emergence in communities of deep network agents assigned a joint task, hoping to gain insights on human language evolution. We propose here a new task capturing crucial aspects of the human environment, such as natural object affordances, and of human conversation, such as full sym... | 816de9ce76fd8528a2058648eaeebef4 | 2,019 | [
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ACL | Generating Counter Narratives against Online Hate Speech: Data and Strategies | Recently research has started focusing on avoiding undesired effects that come with content moderation, such as censorship and overblocking, when dealing with hatred online. The core idea is to directly intervene in the discussion with textual responses that are meant to counter the hate content and prevent it from fur... | 5d266bae5879098654c58c3486c10e7a | 2,020 | [
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ACL | Modeling Multi-hop Question Answering as Single Sequence Prediction | Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state of the art on single-hop QA. However, the complexity of multi-hop QA hinders the effectiveness of the generative QA approach. In this work,... | 7d854805dd99f2bd091f7785960eb618 | 2,022 | [
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ACL | Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger | Backdoor attacks are a kind of insidious security threat against machine learning models. After being injected with a backdoor in training, the victim model will produce adversary-specified outputs on the inputs embedded with predesigned triggers but behave properly on normal inputs during inference. As a sort of emerg... | 67a1967356c54f8dc9f9f609cd4fffd2 | 2,021 | [
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ACL | Content Word Aware Neural Machine Translation | Neural machine translation (NMT) encodes the source sentence in a universal way to generate the target sentence word-by-word. However, NMT does not consider the importance of word in the sentence meaning, for example, some words (i.e., content words) express more important meaning than others (i.e., function words). To... | c1198384f32a65b9f55d40a8d763662e | 2,020 | [
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ACL | Curriculum Learning for Natural Language Understanding | With the great success of pre-trained language models, the pretrain-finetune paradigm now becomes the undoubtedly dominant solution for natural language understanding (NLU) tasks. At the fine-tune stage, target task data is usually introduced in a completely random order and treated equally. However, examples in NLU ta... | 4e26ea76010caa9adf453119b6589e84 | 2,020 | [
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ACL | SemBleu: A Robust Metric for AMR Parsing Evaluation | Evaluating AMR parsing accuracy involves comparing pairs of AMR graphs. The major evaluation metric, SMATCH (Cai and Knight, 2013), searches for one-to-one mappings between the nodes of two AMRs with a greedy hill-climbing algorithm, which leads to search errors. We propose SEMBLEU, a robust metric that extends BLEU (P... | b4b61677ca73f9120640ec6e23712a2c | 2,019 | [
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ACL | Unsupervised Paraphrasing without Translation | Paraphrasing is an important task demonstrating the ability to abstract semantic content from its surface form. Recent literature on automatic paraphrasing is dominated by methods leveraging machine translation as an intermediate step. This contrasts with humans, who can paraphrase without necessarily being bilingual. ... | 8eaf280e1051fc86e7a3a53e7d121610 | 2,019 | [
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ACL | Rationalizing Medical Relation Prediction from Corpus-level Statistics | Nowadays, the interpretability of machine learning models is becoming increasingly important, especially in the medical domain. Aiming to shed some light on how to rationalize medical relation prediction, we present a new interpretable framework inspired by existing theories on how human memory works, e.g., theories of... | e9a9590f8c1803472457619a8c961772 | 2,020 | [
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ACL | Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder | Operational risk management is one of the biggest challenges nowadays faced by financial institutions. There are several major challenges of building a text classification system for automatic operational risk prediction, including imbalanced labeled/unlabeled data and lacking interpretability. To tackle these challeng... | 9223eb08213a0d2decc17a9991526e7d | 2,020 | [
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ACL | Generating Summaries with Topic Templates and Structured Convolutional Decoders | Existing neural generation approaches create multi-sentence text as a single sequence. In this paper we propose a structured convolutional decoder that is guided by the content structure of target summaries. We compare our model with existing sequential decoders on three data sets representing different domains. Automa... | 63d741667a3dc56e441475e4f0594518 | 2,019 | [
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ACL | Learning to Control the Fine-grained Sentiment for Story Ending Generation | Automatic story ending generation is an interesting and challenging task in natural language generation. Previous studies are mainly limited to generate coherent, reasonable and diversified story endings, and few works focus on controlling the sentiment of story endings. This paper focuses on generating a story ending ... | 0e3715411cf0b2ea2811bc5df5873ef4 | 2,019 | [
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ACL | Memorisation versus Generalisation in Pre-trained Language Models | State-of-the-art pre-trained language models have been shown to memorise facts and perform well with limited amounts of training data. To gain a better understanding of how these models learn, we study their generalisation and memorisation capabilities in noisy and low-resource scenarios. We find that the training of t... | 262e849f025fdca876c4df52e4abd06d | 2,022 | [
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ACL | Long-Span Summarization via Local Attention and Content Selection | Transformer-based models have achieved state-of-the-art results in a wide range of natural language processing (NLP) tasks including document summarization. Typically these systems are trained by fine-tuning a large pre-trained model to the target task. One issue with these transformer-based models is that they do not ... | f944b53585a9002ecd6bd9c9f0a12066 | 2,021 | [
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ACL | Heterogeneous Graph Neural Networks for Extractive Document Summarization | As a crucial step in extractive document summarization, learning cross-sentence relations has been explored by a plethora of approaches. An intuitive way is to put them in the graph-based neural network, which has a more complex structure for capturing inter-sentence relationships. In this paper, we present a heterogen... | df1d46eff81fdaa64ed6d41c48a62fc3 | 2,020 | [
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ACL | When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation | Subword segmentation algorithms have been a de facto choice when building neural machine translation systems. However, most of them need to learn a segmentation model based on some heuristics, which may produce sub-optimal segmentation. This can be problematic in some scenarios when the target language has rich morphol... | bed6e5b0be66bea2fdb2483c13f6af6f | 2,021 | [
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ACL | COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic | We introduce a FEVER-like dataset COVID-Fact of 4,086 claims concerning the COVID-19 pandemic. The dataset contains claims, evidence for the claims, and contradictory claims refuted by the evidence. Unlike previous approaches, we automatically detect true claims and their source articles and then generate counter-claim... | 150dde9f7c0ef77a81cce1e0f4c96d81 | 2,021 | [
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ACL | Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction | Despite the recent progress, little is known about the features captured by state-of-the-art neural relation extraction (RE) models. Common methods encode the source sentence, conditioned on the entity mentions, before classifying the relation. However, the complexity of the task makes it difficult to understand how en... | a7581a07fe98de2af71d8aa6b0960f78 | 2,020 | [
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ACL | Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning | Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult. We study interactive weakly-supervised learning—the problem of iteratively and automatically discovering novel lab... | 0230783e4c750545dc027b1638a7db56 | 2,022 | [
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ACL | Reasoning over Entity-Action-Location Graph for Procedural Text Understanding | Procedural text understanding aims at tracking the states (e.g., create, move, destroy) and locations of the entities mentioned in a given paragraph. To effectively track the states and locations, it is essential to capture the rich semantic relations between entities, actions, and locations in the paragraph. Although ... | 91231756955781a6e28747a5e7020e78 | 2,021 | [
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ACL | Are Pretrained Convolutions Better than Pretrained Transformers? | In the era of pre-trained language models, Transformers are the de facto choice of model architectures. While recent research has shown promise in entirely convolutional, or CNN, architectures, they have not been explored using the pre-train-fine-tune paradigm. In the context of language models, are convolutional model... | 068395c590ca4cf55361670e36890101 | 2,021 | [
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