pwc-archive/papers-with-abstracts huggingface dataset:
Annotating Online Misogyny
Authors: Philine Zeinert, Nanna Inie, Leon Derczynski
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Abusive Language, Hate Speech Detection

Online misogyny, a category of online abusive language, has serious and harmful social consequences. Automatic detection of misogynistic language online, while imperative, poses complicated challenges to both data gathering, data annotation, and bias mitigation, as this type of data is linguistically complex and diverse. This paper makes three contributions in this area: Firstly, we describe the detailed design of our iterative annotation process and codebook. Secondly, we present a comprehensive taxonomy of labels for annotating misogyny in natural written language, and finally, we introduce a high-quality dataset of annotated posts sampled from social media posts.
MultiMET: A Multimodal Dataset for Metaphor Understanding
Authors: Dongyu Zhang, Minghao Zhang, Heting Zhang, Liang Yang, Hongfei Lin
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5

Metaphor involves not only a linguistic phenomenon, but also a cognitive phenomenon structuring human thought, which makes understanding it challenging. As a means of cognition, metaphor is rendered by more than texts alone, and multimodal information in which vision/audio content is integrated with the text can play an important role in expressing and understanding metaphor. However, previous metaphor processing and understanding has focused on texts, partly due to the unavailability of large-scale datasets with ground truth labels of multimodal metaphor. In this paper, we introduce MultiMET, a novel multimodal metaphor dataset to facilitate understanding metaphorical information from multimodal text and image. It contains 10,437 text-image pairs from a range of sources with multimodal annotations of the occurrence of metaphors, domain relations, sentiments metaphors convey, and author intents. MultiMET opens the door to automatic metaphor understanding by investigating multimodal cues and their interplay. Moreover, we propose a range of strong baselines and show the importance of combining multimodal cues for metaphor understanding. MultiMET will be released publicly for research.
Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering
Authors: Yunshi Lan, Jing Jiang
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Graph Neural Network, Knowledge Base Question Answering, Question Answering

Conversational KBQA is about answering a sequence of questions related to a KB. Follow-up questions in conversational KBQA often have missing information referring to entities from the conversation history. In this paper, we propose to model these implied entities, which we refer to as the focal entities of the conversation. We propose a novel graph-based model to capture the transitions of focal entities and apply a graph neural network to derive a probability distribution of focal entities for each question, which is then combined with a standard KBQA module to perform answer ranking. Our experiments on two datasets demonstrate the effectiveness of our proposed method.
Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation
Authors: Shizhe Diao, Ruijia Xu, Hongjin Su, Yilei Jiang, Yan Song, Tong Zhang
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Domain Adaptation, Time Series Forecasting

Large pre-trained models such as BERT are known to improve different downstream NLP tasks, even when such a model is trained on a generic domain. Moreover, recent studies have shown that when large domain-specific corpora are available, continued pre-training on domain-specific data can further improve the performance of in-domain tasks. However, this practice requires significant domain-specific data and computational resources which may not always be available. In this paper, we aim to adapt a generic pretrained model with a relatively small amount of domain-specific data. We demonstrate that by explicitly incorporating multi-granularity information of unseen and domain-specific words via the adaptation of (word based) n-grams, the performance of a generic pretrained model can be greatly improved. Specifically, we introduce a Transformer-based Domain-aware N-gram Adaptor, T-DNA, to effectively learn and incorporate the semantic representation of different combinations of words in the new domain. Experimental results illustrate the effectiveness of T-DNA on eight low-resource downstream tasks from four domains. We show that T-DNA is able to achieve significant improvements compared to existing methods on most tasks using limited data with lower computational costs. Moreover, further analyses demonstrate the importance and effectiveness of both unseen words and the information of different granularities. Our code is available at https://github.com/shizhediao/T-DNA.
Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path
Authors: Yiran Wang, Hiroyuki Shindo, Yuji Matsumoto, Taro Watanabe
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Nested Named Entity Recognition

This paper presents a novel method for nested named entity recognition. As a layered method, our method extends the prior second-best path recognition method by explicitly excluding the influence of the best path. Our method maintains a set of hidden states at each time step and selectively leverages them to build a different potential function for recognition at each level. In addition, we demonstrate that recognizing innermost entities first results in better performance than the conventional outermost entities first scheme. We provide extensive experimental results on ACE2004, ACE2005, and GENIA datasets to show the effectiveness and efficiency of our proposed method.
Value-Agnostic Conversational Semantic Parsing
Authors: Emmanouil Antonios Platanios, Adam Pauls, Subhro Roy, Yuchen Zhang, Alexander Kyte, Alan Guo, Sam Thomson, Jayant Krishnamurthy, Jason Wolfe, Jacob Andreas, Dan Klein
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Computational Efficiency, Semantic Parsing

Conversational semantic parsers map user utterances to executable programs given dialogue histories composed of previous utterances, programs, and system responses. Existing parsers typically condition on rich representations of history that include the complete set of values and computations previously discussed. We propose a model that abstracts over values to focus prediction on type- and function-level context. This approach provides a compact encoding of dialogue histories and predicted programs, improving generalization and computational efficiency. Our model incorporates several other components, including an atomic span copy operation and structural enforcement of well-formedness constraints on predicted programs, that are particularly advantageous in the low-data regime. Trained on the SMCalFlow and TreeDST datasets, our model outperforms prior work by 7.3{\%} and 10.6{\%} respectively in terms of absolute accuracy. Trained on only a thousand examples from each dataset, it outperforms strong baselines by 12.4{\%} and 6.4{\%}. These results indicate that simple representations are key to effective generalization in conversational semantic parsing.
Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental
Authors: Morteza Rohanian, Julian Hough
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Language Modelling, Sentence, speech-recognition, Speech Recognition

While Transformer-based text classifiers pre-trained on large volumes of text have yielded significant improvements on a wide range of computational linguistics tasks, their implementations have been unsuitable for live incremental processing thus far, operating only on the level of complete sentence inputs. We address the challenge of introducing methods for word-by-word left-to-right incremental processing to Transformers such as BERT, models without an intrinsic sense of linear order. We modify the training method and live decoding of non-incremental models to detect speech disfluencies with minimum latency and without pre-segmentation of dialogue acts. We experiment with several decoding methods to predict the rightward context of the word currently being processed using a GPT-2 language model and apply a BERT-based disfluency detector to sequences, including predicted words. We show our method of incrementalising Transformers maintains most of their high non-incremental performance while operating strictly incrementally. We also evaluate our models{'} incremental performance to establish the trade-off between incremental performance and final performance, using different prediction strategies. We apply our system to incremental speech recognition results as they arrive into a live system and achieve state-of-the-art results in this setting.
CDRNN: Discovering Complex Dynamics in Human Language Processing
Authors: Cory Shain
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: regression

The human mind is a dynamical system, yet many analysis techniques used to study it are limited in their ability to capture the complex dynamics that may characterize mental processes. This study proposes the continuous-time deconvolutional regressive neural network (CDRNN), a deep neural extension of continuous-time deconvolutional regression (Shain {\&} Schuler, 2021) that jointly captures time-varying, non-linear, and delayed influences of predictors (e.g. word surprisal) on the response (e.g. reading time). Despite this flexibility, CDRNN is interpretable and able to illuminate patterns in human cognition that are otherwise difficult to study. Behavioral and fMRI experiments reveal detailed and plausible estimates of human language processing dynamics that generalize better than CDR and other baselines, supporting a potential role for CDRNN in studying human language processing.
Surprisal Estimators for Human Reading Times Need Character Models
Authors: Byung-Doh Oh, Christian Clark, William Schuler
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Sentence

While the use of character models has been popular in NLP applications, it has not been explored much in the context of psycholinguistic modeling. This paper presents a character model that can be applied to a structural parser-based processing model to calculate word generation probabilities. Experimental results show that surprisal estimates from a structural processing model using this character model deliver substantially better fits to self-paced reading, eye-tracking, and fMRI data than those from large-scale language models trained on much more data. This may suggest that the proposed processing model provides a more humanlike account of sentence processing, which assumes a larger role of morphology, phonotactics, and orthographic complexity than was previously thought.
Label-Specific Dual Graph Neural Network for Multi-Label Text Classification
Authors: Qianwen Ma, Chunyuan Yuan, Wei Zhou, Songlin Hu
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Dynamic Reconstruction, Graph Neural Network, Multi Label Text Classification, Multi-Label Text Classification, text-classification, Text Classification

Multi-label text classification is one of the fundamental tasks in natural language processing. Previous studies have difficulties to distinguish similar labels well because they learn the same document representations for different labels, that is they do not explicitly extract label-specific semantic components from documents. Moreover, they do not fully explore the high-order interactions among these semantic components, which is very helpful to predict tail labels. In this paper, we propose a novel label-specific dual graph neural network (LDGN), which incorporates category information to learn label-specific components from documents, and employs dual Graph Convolution Network (GCN) to model complete and adaptive interactions among these components based on the statistical label co-occurrence and dynamic reconstruction graph in a joint way. Experimental results on three benchmark datasets demonstrate that LDGN significantly outperforms the state-of-the-art models, and also achieves better performance with respect to tail labels.
Adversarial Learning for Discourse Rhetorical Structure Parsing
Authors: Longyin Zhang, Fang Kong, Guodong Zhou
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: DRS Parsing, global-optimization

Text-level discourse rhetorical structure (DRS) parsing is known to be challenging due to the notorious lack of training data. Although recent top-down DRS parsers can better leverage global document context and have achieved certain success, the performance is still far from perfect. To our knowledge, all previous DRS parsers make local decisions for either bottom-up node composition or top-down split point ranking at each time step, and largely ignore DRS parsing from the global view point. Obviously, it is not sufficient to build an entire DRS tree only through these local decisions. In this work, we present our insight on evaluating the pros and cons of the entire DRS tree for global optimization. Specifically, based on recent well-performing top-down frameworks, we introduce a novel method to transform both gold standard and predicted constituency trees into tree diagrams with two color channels. After that, we learn an adversarial bot between gold and fake tree diagrams to estimate the generated DRS trees from a global perspective. We perform experiments on both RST-DT and CDTB corpora and use the original Parseval for performance evaluation. The experimental results show that our parser can substantially improve the performance when compared with previous state-of-the-art parsers.
Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation
Authors: Tong Zhang, Long Zhang, Wei Ye, Bo Li, Jinan Sun, Xiaoyu Zhu, Wen Zhao, Shikun Zhang
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Machine Translation, NMT, Translation

This paper proposes a sophisticated neural architecture to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models. By introducing three novel components: Pointer, Disambiguator, and Copier, our method PDC achieves the following merits inherently compared with previous efforts: (1) Pointer leverages the semantic information from bilingual dictionaries, for the first time, to better locate source words whose translation in dictionaries can potentially be used; (2) Disambiguator synthesizes contextual information from the source view and the target view, both of which contribute to distinguishing the proper translation of a specific source word from multiple candidates in dictionaries; (3) Copier systematically connects Pointer and Disambiguator based on a hierarchical copy mechanism seamlessly integrated with Transformer, thereby building an end-to-end architecture that could avoid error propagation problems in alternative pipe-line methods. The experimental results on Chinese-English and English-Japanese benchmarks demonstrate the PDC{'}s overall superiority and effectiveness of each component.
A unified approach to sentence segmentation of punctuated text in many languages
Authors: Rachel Wicks, Matt Post
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Sentence, Sentence segmentation

The sentence is a fundamental unit of text processing. Yet sentences in the wild are commonly encountered not in isolation, but unsegmented within larger paragraphs and documents. Therefore, the first step in many NLP pipelines is \textit{sentence segmentation}. Despite its importance, this step is the subject of relatively little research. There are no standard test sets or even methods for evaluation, leaving researchers and engineers without a clear footing for evaluating and selecting models for the task. Existing tools have relatively small language coverage, and efforts to extend them to other languages are often ad hoc. We introduce a modern context-based modeling approach that provides a solution to the problem of segmenting punctuated text in many languages, and show how it can be trained on noisily-annotated data. We also establish a new 23-language multilingual evaluation set. Our approach exceeds high baselines set by existing methods on prior English corpora (WSJ and Brown corpora), and also performs well on average on our new evaluation set. We release our tool, ersatz, as open source.
Handling Extreme Class Imbalance in Technical Logbook Datasets
Authors: Farhad Akhbardeh, Cecilia Ovesdotter Alm, Marcos Zampieri, Travis Desell
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5

Technical logbooks are a challenging and under-explored text type in automated event identification. These texts are typically short and written in non-standard yet technical language, posing challenges to off-the-shelf NLP pipelines. The granularity of issue types described in these datasets additionally leads to class imbalance, making it challenging for models to accurately predict which issue each logbook entry describes. In this paper we focus on the problem of technical issue classification by considering logbook datasets from the automotive, aviation, and facilities maintenance domains. We adapt a feedback strategy from computer vision for handling extreme class imbalance, which resamples the training data based on its error in the prediction process. Our experiments show that with statistical significance this feedback strategy provides the best results for four different neural network models trained across a suite of seven different technical logbook datasets from distinct technical domains. The feedback strategy is also generic and could be applied to any learning problem with substantial class imbalances.
Check It Again:Progressive Visual Question Answering via Visual Entailment
Authors: Qingyi Si, Zheng Lin, Ming yu Zheng, Peng Fu, Weiping Wang
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Question Answering, Visual Entailment, Visual Question Answering, Visual Question Answering (VQA)

While sophisticated neural-based models have achieved remarkable success in Visual Question Answering (VQA), these models tend to answer questions only according to superficial correlations between question and answer. Several recent approaches have been developed to address this language priors problem. However, most of them predict the correct answer according to one best output without checking the authenticity of answers. Besides, they only explore the interaction between image and question, ignoring the semantics of candidate answers. In this paper, we propose a select-and-rerank (SAR) progressive framework based on Visual Entailment. Specifically, we first select the candidate answers relevant to the question or the image, then we rerank the candidate answers by a visual entailment task, which verifies whether the image semantically entails the synthetic statement of the question and each candidate answer. Experimental results show the effectiveness of our proposed framework, which establishes a new state-of-the-art accuracy on VQA-CP v2 with a 7.55{\%} improvement.
Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy?
Authors: Abhilasha Ravichander, Alan W Black, Thomas Norton, Shomir Wilson, Norman Sadeh
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5

Privacy plays a crucial role in preserving democratic ideals and personal autonomy. The dominant legal approach to privacy in many jurisdictions is the {``}Notice and Choice{''} paradigm, where privacy policies are the primary instrument used to convey information to users. However, privacy policies are long and complex documents that are difficult for users to read and comprehend. We discuss how language technologies can play an important role in addressing this information gap, reporting on initial progress towards helping three specific categories of stakeholders take advantage of digital privacy policies: consumers, enterprises, and regulators. Our goal is to provide a roadmap for the development and use of language technologies to empower users to reclaim control over their privacy, limit privacy harms, and rally research efforts from the community towards addressing an issue with large social impact. We highlight many remaining opportunities to develop language technologies that are more precise or nuanced in the way in which they use the text of privacy policies.
Anonymisation Models for Text Data: State of the art, Challenges and Future Directions
Authors: Pierre Lison, Ildik{\'o} Pil{\'a}n, David Sanchez, Montserrat Batet, Lilja {\O}vrelid
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Position, Privacy Preserving

This position paper investigates the problem of automated text anonymisation, which is a prerequisite for secure sharing of documents containing sensitive information about individuals. We summarise the key concepts behind text anonymisation and provide a review of current approaches. Anonymisation methods have so far been developed in two fields with little mutual interaction, namely natural language processing and privacy-preserving data publishing. Based on a case study, we outline the benefits and limitations of these approaches and discuss a number of open challenges, such as (1) how to account for multiple types of semantic inferences, (2) how to strike a balance between disclosure risk and data utility and (3) how to evaluate the quality of the resulting anonymisation. We lay out a case for moving beyond sequence labelling models and incorporate explicit measures of disclosure risk into the text anonymisation process.
End-to-End AMR Coreference Resolution
Authors: Qiankun Fu, Linfeng Song, Wenyu Du, Yue Zhang
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Abstract Meaning Representation, coreference-resolution, Coreference Resolution, Sentence, Text Summarization

Although parsing to Abstract Meaning Representation (AMR) has become very popular and AMR has been shown effective on the many sentence-level downstream tasks, little work has studied how to generate AMRs that can represent multi-sentence information. We introduce the first end-to-end AMR coreference resolution model in order to build multi-sentence AMRs. Compared with the previous pipeline and rule-based approaches, our model alleviates error propagation and it is more robust for both in-domain and out-domain situations. Besides, the document-level AMRs obtained by our model can significantly improve over the AMRs generated by a rule-based method (Liu et al., 2015) on text summarization.
Verb Metaphor Detection via Contextual Relation Learning
Authors: Wei Song, Shuhui Zhou, Ruiji Fu, Ting Liu, Lizhen Liu
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Natural Language Understanding, Relation, Relation Classification

Correct natural language understanding requires computers to distinguish the literal and metaphorical senses of a word. Recent neu- ral models achieve progress on verb metaphor detection by viewing it as sequence labeling. In this paper, we argue that it is appropriate to view this task as relation classification between a verb and its various contexts. We propose the Metaphor-relation BERT (Mr-BERT) model, which explicitly models the relation between a verb and its grammatical, sentential and semantic contexts. We evaluate our method on the VUA, MOH-X and TroFi datasets. Our method gets competitive results compared with state-of-the-art approaches.