pwc-archive/papers-with-abstracts huggingface dataset:
EntSUM: A Data Set for Entity-Centric Extractive Summarization
Authors: Mounica Maddela, Mayank Kulkarni, Daniel Preotiuc-Pietro
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Extractive Summarization

Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences to better assist them with their information need, as opposed to the standard summarization setup which build a single generic summary of a document.We introduce a human-annotated data set EntSUM for controllable summarization with a focus on named entities as the aspects to control.We conduct an extensive quantitative analysis to motivate the task of entity-centric summarization and show that existing methods for controllable summarization fail to generate entity-centric summaries. We propose extensions to state-of-the-art summarization approaches that achieve substantially better results on our data set. Our analysis and results show the challenging nature of this task and of the proposed data set.
Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little Cost
Authors: Lihu Chen, Gael Varoquaux, Fabian Suchanek
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Contrastive Learning, Language Modeling, Language Modelling, Word Embeddings

State-of-the-art NLP systems represent inputs with word embeddings, but these are brittle when faced with Out-of-Vocabulary (OOV) words.To address this issue, we follow the principle of mimick-like models to generate vectors for unseen words, by learning the behavior of pre-trained embeddings using only the surface form of words.We present a simple contrastive learning framework, LOVE, which extends the word representation of an existing pre-trained language model (such as BERT) and makes it robust to OOV with few additional parameters.Extensive evaluations demonstrate that our lightweight model achieves similar or even better performances than prior competitors, both on original datasets and on corrupted variants. Moreover, it can be used in a plug-and-play fashion with FastText and BERT, where it significantly improves their robustness.
Upstream Mitigation Is Not All You Need: Testing the Bias Transfer Hypothesis in Pre-Trained Language Models
Authors: Ryan Steed, Swetasudha Panda, Ari Kobren, Michael Wick
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: All

A few large, homogenous, pre-trained models undergird many machine learning systems — and often, these models contain harmful stereotypes learned from the internet. We investigate the bias transfer hypothesis: the theory that social biases (such as stereotypes) internalized by large language models during pre-training transfer into harmful task-specific behavior after fine-tuning. For two classification tasks, we find that reducing intrinsic bias with controlled interventions before fine-tuning does little to mitigate the classifier’s discriminatory behavior after fine-tuning. Regression analysis suggests that downstream disparities are better explained by biases in the fine-tuning dataset. Still, pre-training plays a role: simple alterations to co-occurrence rates in the fine-tuning dataset are ineffective when the model has been pre-trained. Our results encourage practitioners to focus more on dataset quality and context-specific harms.
Improving Multi-label Malevolence Detection in Dialogues through Multi-faceted Label Correlation Enhancement
Authors: Yangjun Zhang, Pengjie Ren, Wentao Deng, Zhumin Chen, Maarten Rijke
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5

A dialogue response is malevolent if it is grounded in negative emotions, inappropriate behavior, or an unethical value basis in terms of content and dialogue acts. The detection of malevolent dialogue responses is attracting growing interest. Current research on detecting dialogue malevolence has limitations in terms of datasets and methods. First, available dialogue datasets related to malevolence are labeled with a single category, but in practice assigning a single category to each utterance may not be appropriate as some malevolent utterances belong to multiple labels. Second, current methods for detecting dialogue malevolence neglect label correlation. Therefore, we propose the task of multi-label dialogue malevolence detection and crowdsource a multi-label dataset, multi-label dialogue malevolence detection (MDMD) for evaluation. We also propose a multi-label malevolence detection model, multi-faceted label correlation enhanced CRF (MCRF), with two label correlation mechanisms, label correlation in taxonomy (LCT) and label correlation in context (LCC). Experiments on MDMD show that our method outperforms the best performing baseline by a large margin, i.e., 16.1%, 11.9%, 12.0%, and 6.1% on precision, recall, F1, and Jaccard score, respectively.
Prompt-free and Efficient Few-shot Learning with Language Models
Authors: Rabeeh Karimi Mahabadi, Luke Zettlemoyer, James Henderson, Lambert Mathias, Marzieh Saeidi, Veselin Stoyanov, Majid Yazdani
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Few-Shot Learning

Current methods for few-shot fine-tuning of pretrained masked language models (PLMs) require carefully engineered prompts and verbalizers for each new task to convert examples into a cloze-format that the PLM can score. In this work, we propose Perfect, a simple and efficient method for few-shot fine-tuning of PLMs without relying on any such handcrafting, which is highly effective given as few as 32 data points. Perfect makes two key design choices: First, we show that manually engineered task prompts can be replaced with task-specific adapters that enable sample-efficient fine-tuning and reduce memory and storage costs by roughly factors of 5 and 100, respectively. Second, instead of using handcrafted verbalizers, we learn new multi-token label embeddings during fine-tuning, which are not tied to the model vocabulary and which allow us to avoid complex auto-regressive decoding. These embeddings are not only learnable from limited data but also enable nearly 100x faster training and inference. Experiments on a wide range of few shot NLP tasks demonstrate that Perfect, while being simple and efficient, also outperforms existing state-of-the-art few-shot learning methods. Our code is publicly available at https://github.com/rabeehk/perfect.
From the Detection of Toxic Spans in Online Discussions to the Analysis of Toxic-to-Civil Transfer
Authors: John Pavlopoulos, Leo Laugier, Alexandros Xenos, Jeffrey Sorensen, Ion Androutsopoulos
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Toxic Spans Detection

We study the task of toxic spans detection, which concerns the detection of the spans that make a text toxic, when detecting such spans is possible. We introduce a dataset for this task, ToxicSpans, which we release publicly. By experimenting with several methods, we show that sequence labeling models perform best, but methods that add generic rationale extraction mechanisms on top of classifiers trained to predict if a post is toxic or not are also surprisingly promising. Finally, we use ToxicSpans and systems trained on it, to provide further analysis of state-of-the-art toxic to non-toxic transfer systems, as well as of human performance on that latter task. Our work highlights challenges in finer toxicity detection and mitigation.
Is Attention Explanation? An Introduction to the Debate
Authors: Adrien Bibal, Rémi Cardon, David Alfter, Rodrigo Wilkens, Xiaoou Wang, Thomas François, Patrick Watrin
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5

The performance of deep learning models in NLP and other fields of machine learning has led to a rise in their popularity, and so the need for explanations of these models becomes paramount. Attention has been seen as a solution to increase performance, while providing some explanations. However, a debate has started to cast doubt on the explanatory power of attention in neural networks. Although the debate has created a vast literature thanks to contributions from various areas, the lack of communication is becoming more and more tangible. In this paper, we provide a clear overview of the insights on the debate by critically confronting works from these different areas. This holistic vision can be of great interest for future works in all the communities concerned by this debate. We sum up the main challenges spotted in these areas, and we conclude by discussing the most promising future avenues on attention as an explanation.
There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory
Authors: Tingchen Fu, Xueliang Zhao, Chongyang Tao, Ji-Rong Wen, Rui Yan
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Chatbot

Knowledge-grounded conversation (KGC) shows great potential in building an engaging and knowledgeable chatbot, and knowledge selection is a key ingredient in it. However, previous methods for knowledge selection only concentrate on the relevance between knowledge and dialogue context, ignoring the fact that age, hobby, education and life experience of an interlocutor have a major effect on his or her personal preference over external knowledge. Without taking the personalization issue into account, it is difficult for existing dialogue systems to select the proper knowledge and generate persona-consistent responses.In this work, we introduce personal memory into knowledge selection in KGC to address the personalization issue. We propose a variational method to model the underlying relationship between one’s personal memory and his or her selection of knowledge, and devise a learning scheme in which the forward mapping from personal memory to knowledge and its inverse mapping is included in a closed loop so that they could teach each other. Experiment results show that our methods outperform existing KGC methods significantly on both automatic evaluation and human evaluation.
Incorporating Stock Market Signals for Twitter Stance Detection
Authors: Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, Nigel Collier
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Stance Detection, Time Series, Time Series Analysis

Research in stance detection has so far focused on models which leverage purely textual input. In this paper, we investigate the integration of textual and financial signals for stance detection in the financial domain. Specifically, we propose a robust multi-task neural architecture that combines textual input with high-frequency intra-day time series from stock market prices. Moreover, we extend wt–wt, an existing stance detection dataset which collects tweets discussing Mergers and Acquisitions operations, with the relevant financial signal. Importantly, the obtained dataset aligns with Stander, an existing news stance detection dataset, thus resulting in a unique multimodal, multi-genre stance detection resource. We show experimentally and through detailed result analysis that our stance detection system benefits from financial information, and achieves state-of-the-art results on the wt–wt dataset: this demonstrates that the combination of multiple input signals is effective for cross-target stance detection, and opens interesting research directions for future work.
Word Segmentation as Unsupervised Constituency Parsing
Authors: Raquel Alhama
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Constituency Parsing, Relation, Segmentation

Word identification from continuous input is typically viewed as a segmentation task. Experiments with human adults suggest that familiarity with syntactic structures in their native language also influences word identification in artificial languages; however, the relation between syntactic processing and word identification is yet unclear. This work takes one step forward by exploring a radically different approach of word identification, in which segmentation of a continuous input is viewed as a process isomorphic to unsupervised constituency parsing. Besides formalizing the approach, this study reports simulations of human experiments with DIORA (Drozdov et al., 2020), a neural unsupervised constituency parser. Results show that this model can reproduce human behavior in word identification experiments, suggesting that this is a viable approach to study word identification and its relation to syntactic processing.
SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems
Authors: Emily Dinan, Gavin Abercrombie, A. Bergman, Shannon Spruit, Dirk Hovy, Y-Lan Boureau, Verena Rieser
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Position

The social impact of natural language processing and its applications has received increasing attention. In this position paper, we focus on the problem of safety for end-to-end conversational AI. We survey the problem landscape therein, introducing a taxonomy of three observed phenomena: the Instigator, Yea-Sayer, and Impostor effects. We then empirically assess the extent to which current tools can measure these effects and current systems display them. We release these tools as part of a “first aid kit” (SafetyKit) to quickly assess apparent safety concerns. Our results show that, while current tools are able to provide an estimate of the relative safety of systems in various settings, they still have several shortcomings. We suggest several future directions and discuss ethical considerations.
MMCoQA: Conversational Question Answering over Text, Tables, and Images
Authors: Yongqi Li, Wenjie Li, Liqiang Nie
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Benchmarking, Conversational Question Answering, Question Answering, Retrieval

The rapid development of conversational assistants accelerates the study on conversational question answering (QA). However, the existing conversational QA systems usually answer users’ questions with a single knowledge source, e.g., paragraphs or a knowledge graph, but overlook the important visual cues, let alone multiple knowledge sources of different modalities. In this paper, we hence define a novel research task, i.e., multimodal conversational question answering (MMCoQA), aiming to answer users’ questions with multimodal knowledge sources via multi-turn conversations. This new task brings a series of research challenges, including but not limited to priority, consistency, and complementarity of multimodal knowledge. To facilitate the data-driven approaches in this area, we construct the first multimodal conversational QA dataset, named MMConvQA. Questions are fully annotated with not only natural language answers but also the corresponding evidence and valuable decontextualized self-contained questions. Meanwhile, we introduce an end-to-end baseline model, which divides this complex research task into question understanding, multi-modal evidence retrieval, and answer extraction. Moreover, we report a set of benchmarking results, and the results indicate that there is ample room for improvement.
RST Discourse Parsing with Second-Stage EDU-Level Pre-training
Authors: Nan Yu, Meishan Zhang, Guohong Fu, Min Zhang
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Discourse Marker Prediction, Discourse Parsing, Sentence

Pre-trained language models (PLMs) have shown great potentials in natural language processing (NLP) including rhetorical structure theory (RST) discourse parsing.Current PLMs are obtained by sentence-level pre-training, which is different from the basic processing unit, i.e. element discourse unit (EDU).To this end, we propose a second-stage EDU-level pre-training approach in this work, which presents two novel tasks to learn effective EDU representations continually based on well pre-trained language models.Concretely, the two tasks are (1) next EDU prediction (NEP) and (2) discourse marker prediction (DMP).We take a state-of-the-art transition-based neural parser as baseline, and adopt it with a light bi-gram EDU modification to effectively explore the EDU-level pre-trained EDU representation.Experimental results on a benckmark dataset show that our method is highly effective,leading a 2.1-point improvement in F1-score.All codes and pre-trained models will be released publicly to facilitate future studies.
DiBiMT: A Novel Benchmark for Measuring Word Sense Disambiguation Biases in Machine Translation
Authors: Niccolò Campolungo, Federico Martelli, Francesco Saina, Roberto Navigli
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Machine Translation, Translation, Word Sense Disambiguation

Lexical ambiguity poses one of the greatest challenges in the field of Machine Translation. Over the last few decades, multiple efforts have been undertaken to investigate incorrect translations caused by the polysemous nature of words. Within this body of research, some studies have posited that models pick up semantic biases existing in the training data, thus producing translation errors. In this paper, we present DiBiMT, the first entirely manually-curated evaluation benchmark which enables an extensive study of semantic biases in Machine Translation of nominal and verbal words in five different language combinations, namely, English and one or other of the following languages: Chinese, German, Italian, Russian and Spanish. Furthermore, we test state-of-the-art Machine Translation systems, both commercial and non-commercial ones, against our new test bed and provide a thorough statistical and linguistic analysis of the results. We release DiBiMT at https://nlp.uniroma1.it/dibimt as a closed benchmark with a public leaderboard.
EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers
Authors: Bugeun Kim, Kyung Seo Ki, Sangkyu Rhim, Gahgene Gweon
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Math, Math Word Problem Solving

In this paper, we propose a neural model EPT-X (Expression-Pointer Transformer with Explanations), which utilizes natural language explanations to solve an algebraic word problem. To enhance the explainability of the encoding process of a neural model, EPT-X adopts the concepts of plausibility and faithfulness which are drawn from math word problem solving strategies by humans. A plausible explanation is one that includes contextual information for the numbers and variables that appear in a given math word problem. A faithful explanation is one that accurately represents the reasoning process behind the model’s solution equation. The EPT-X model yields an average baseline performance of 69.59% on our PEN dataset and produces explanations with quality that is comparable to human output. The contribution of this work is two-fold. (1) EPT-X model: An explainable neural model that sets a baseline for algebraic word problem solving task, in terms of model’s correctness, plausibility, and faithfulness. (2) New dataset: We release a novel dataset PEN (Problems with Explanations for Numbers), which expands the existing datasets by attaching explanations to each number/variable.
Identifying the Human Values behind Arguments
Authors: Johannes Kiesel, Milad Alshomary, Nicolas Handke, Xiaoni Cai, Henning Wachsmuth, Benno Stein
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Argument Mining

This paper studies the (often implicit) human values behind natural language arguments, such as to have freedom of thought or to be broadminded. Values are commonly accepted answers to why some option is desirable in the ethical sense and are thus essential both in real-world argumentation and theoretical argumentation frameworks. However, their large variety has been a major obstacle to modeling them in argument mining. To overcome this obstacle, we contribute an operationalization of human values, namely a multi-level taxonomy with 54 values that is in line with psychological research. Moreover, we provide a dataset of 5270 arguments from four geographical cultures, manually annotated for human values. First experiments with the automatic classification of human values are promising, with F_1-scores up to 0.81 and 0.25 on average.
Saliency as Evidence: Event Detection with Trigger Saliency Attribution
Authors: Jian Liu, Yufeng Chen, Jinan Xu
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Event Detection, Event Extraction

Event detection (ED) is a critical subtask of event extraction that seeks to identify event triggers of certain types in texts.Despite significant advances in ED, existing methods typically follow a “one model fits all types” approach, which sees no differences between event types and often results in a quite skewed performance.Finding the causes of skewed performance is crucial for the robustness of an ED model, but to date there has been little exploration of this problem.This research examines the issue in depth and presents a new concept termed trigger salience attribution, which can explicitly quantify the underlying patterns of events. On this foundation, we develop a new training mechanism for ED, which can distinguish between trigger-dependent and context-dependent types and achieve promising performance on two benchmarks.Finally, by highlighting many distinct characteristics of trigger-dependent and context-dependent types, our work may promote more research into this problem.
SRL4E – Semantic Role Labeling for Emotions: A Unified Evaluation Framework
Authors: Cesare Campagnano, Simone Conia, Roberto Navigli
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Semantic Role Labeling, Sentence, Sentiment Analysis

In the field of sentiment analysis, several studies have highlighted that a single sentence may express multiple, sometimes contrasting, sentiments and emotions, each with its own experiencer, target and/or cause. To this end, over the past few years researchers have started to collect and annotate data manually, in order to investigate the capabilities of automatic systems not only to distinguish between emotions, but also to capture their semantic constituents. However, currently available gold datasets are heterogeneous in size, domain, format, splits, emotion categories and role labels, making comparisons across different works difficult and hampering progress in the area. In this paper, we tackle this issue and present a unified evaluation framework focused on Semantic Role Labeling for Emotions (SRL4E), in which we unify several datasets tagged with emotions and semantic roles by using a common labeling scheme. We use SRL4E as a benchmark to evaluate how modern pretrained language models perform and analyze where we currently stand in this task, hoping to provide the tools to facilitate studies in this complex area.
Context Matters: A Pragmatic Study of PLMs’ Negation Understanding
Authors: Reto Gubelmann, Siegfried Handschuh
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Negation

In linguistics, there are two main perspectives on negation: a semantic and a pragmatic view. So far, research in NLP on negation has almost exclusively adhered to the semantic view. In this article, we adopt the pragmatic paradigm to conduct a study of negation understanding focusing on transformer-based PLMs. Our results differ from previous, semantics-based studies and therefore help to contribute a more comprehensive – and, given the results, much more optimistic – picture of the PLMs’ negation understanding.