pwc-archive/papers-with-abstracts huggingface dataset:
Probing for Predicate Argument Structures in Pretrained Language Models
Authors: Simone Conia, Roberto Navigli
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Semantic Role Labeling

Thanks to the effectiveness and wide availability of modern pretrained language models (PLMs), recently proposed approaches have achieved remarkable results in dependency- and span-based, multilingual and cross-lingual Semantic Role Labeling (SRL). These results have prompted researchers to investigate the inner workings of modern PLMs with the aim of understanding how, where, and to what extent they encode information about SRL. In this paper, we follow this line of research and probe for predicate argument structures in PLMs. Our study shows that PLMs do encode semantic structures directly into the contextualized representation of a predicate, and also provides insights into the correlation between predicate senses and their structures, the degree of transferability between nominal and verbal structures, and how such structures are encoded across languages. Finally, we look at the practical implications of such insights and demonstrate the benefits of embedding predicate argument structure information into an SRL model.
Rare and Zero-shot Word Sense Disambiguation using Z-Reweighting
Authors: Ying Su, Hongming Zhang, Yangqiu Song, Tong Zhang
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Relation, Word Sense Disambiguation

Word sense disambiguation (WSD) is a crucial problem in the natural language processing (NLP) community. Current methods achieve decent performance by utilizing supervised learning and large pre-trained language models. However, the imbalanced training dataset leads to poor performance on rare senses and zero-shot senses. There are more training instances and senses for words with top frequency ranks than those with low frequency ranks in the training dataset. We investigate the statistical relation between word frequency rank and word sense number distribution. Based on the relation, we propose a Z-reweighting method on the word level to adjust the training on the imbalanced dataset. The experiments show that the Z-reweighting strategy achieves performance gain on the standard English all words WSD benchmark. Moreover, the strategy can help models generalize better on rare and zero-shot senses.
Nibbling at the Hard Core of Word Sense Disambiguation
Authors: Marco Maru, Simone Conia, Michele Bevilacqua, Roberto Navigli
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Word Sense Disambiguation

With state-of-the-art systems having finally attained estimated human performance, Word Sense Disambiguation (WSD) has now joined the array of Natural Language Processing tasks that have seemingly been solved, thanks to the vast amounts of knowledge encoded into Transformer-based pre-trained language models. And yet, if we look below the surface of raw figures, it is easy to realize that current approaches still make trivial mistakes that a human would never make. In this work, we provide evidence showing why the F1 score metric should not simply be taken at face value and present an exhaustive analysis of the errors that seven of the most representative state-of-the-art systems for English all-words WSD make on traditional evaluation benchmarks.In addition, we produce and release a collection of test sets featuring (a) an amended version of the standard evaluation benchmark that fixes its lexical and semantic inaccuracies, (b) 42D, a challenge set devised to assess the resilience of systems with respect to least frequent word senses and senses not seen at training time, and (c) hardEN, a challenge set made up solely of instances which none of the investigated state-of-the-art systems can solve. We make all of the test sets and model predictions available to the research community at https://github.com/SapienzaNLP/wsd-hard-benchmark.
Unsupervised Dependency Graph Network
Authors: Yikang Shen, Shawn Tan, Alessandro Sordoni, Peng Li, Jie zhou, Aaron Courville
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Dependency Parsing, Language Modeling, Language Modelling, Masked Language Modeling, POS, Sentence, Unsupervised Dependency Parsing

Recent work has identified properties of pretrained self-attention models that mirror those of dependency parse structures. In particular, some self-attention heads correspond well to individual dependency types. Inspired by these developments, we propose a new competitive mechanism that encourages these attention heads to model different dependency relations. We introduce a new model, the Unsupervised Dependency Graph Network (UDGN), that can induce dependency structures from raw corpora and the masked language modeling task. Experiment results show that UDGN achieves very strong unsupervised dependency parsing performance without gold POS tags and any other external information. The competitive gated heads show a strong correlation with human-annotated dependency types. Furthermore, the UDGN can also achieve competitive performance on masked language modeling and sentence textual similarity tasks.
Textomics: A Dataset for Genomics Data Summary Generation
Authors: Mu-Chun Wang, Zixuan Liu, Sheng Wang
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5

Summarizing biomedical discovery from genomics data using natural languages is an essential step in biomedical research but is mostly done manually. Here, we introduce Textomics, a novel dataset of genomics data description, which contains 22,273 pairs of genomics data matrices and their summaries. Each summary is written by the researchers who generated the data and associated with a scientific paper. Based on this dataset, we study two novel tasks: generating textual summary from a genomics data matrix and vice versa. Inspired by the successful applications of k nearest neighbors in modeling genomics data, we propose a kNN-Vec2Text model to address these tasks and observe substantial improvement on our dataset. We further illustrate how Textomics can be used to advance other applications, including evaluating scientific paper embeddings and generating masked templates for scientific paper understanding. Textomics serves as the first benchmark for generating textual summaries for genomics data and we envision it will be broadly applied to other biomedical and natural language processing applications.
A Contrastive Framework for Learning Sentence Representations from Pairwise and Triple-wise Perspective in Angular Space
Authors: Yuhao Zhang, Hongji Zhu, Yongliang Wang, Nan Xu, Xiaobo Li, Binqiang Zhao
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Contrastive Learning, Semantic Textual Similarity, Sentence, STS, Triplet

Learning high-quality sentence representations is a fundamental problem of natural language processing which could benefit a wide range of downstream tasks. Though the BERT-like pre-trained language models have achieved great success, using their sentence representations directly often results in poor performance on the semantic textual similarity task. Recently, several contrastive learning methods have been proposed for learning sentence representations and have shown promising results. However, most of them focus on the constitution of positive and negative representation pairs and pay little attention to the training objective like NT-Xent, which is not sufficient enough to acquire the discriminating power and is unable to model the partial order of semantics between sentences. So in this paper, we propose a new method ArcCSE, with training objectives designed to enhance the pairwise discriminative power and model the entailment relation of triplet sentences. We conduct extensive experiments which demonstrate that our approach outperforms the previous state-of-the-art on diverse sentence related tasks, including STS and SentEval.
Learning From Failure: Data Capture in an Australian Aboriginal Community
Authors: Eric Le Ferrand, Steven Bird, Laurent Besacier
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5

Most low resource language technology development is premised on the need to collect data for training statistical models. When we follow the typical process of recording and transcribing text for small Indigenous languages, we hit up against the so-called “transcription bottleneck.” Therefore it is worth exploring new ways of engaging with speakers which generate data while avoiding the transcription bottleneck. We have deployed a prototype app for speakers to use for confirming system guesses in an approach to transcription based on word spotting. However, in the process of testing the app we encountered many new problems for engagement with speakers. This paper presents a close-up study of the process of deploying data capture technology on the ground in an Australian Aboriginal community. We reflect on our interactions with participants and draw lessons that apply to anyone seeking to develop methods for language data collection in an Indigenous community.
Deep Inductive Logic Reasoning for Multi-Hop Reading Comprehension
Authors: Wenya Wang, Sinno Pan
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Deep Learning, Multi-Hop Reading Comprehension, Reading Comprehension, Relational Reasoning

Multi-hop reading comprehension requires an ability to reason across multiple documents. On the one hand, deep learning approaches only implicitly encode query-related information into distributed embeddings which fail to uncover the discrete relational reasoning process to infer the correct answer. On the other hand, logic-based approaches provide interpretable rules to infer the target answer, but mostly work on structured data where entities and relations are well-defined. In this paper, we propose a deep-learning based inductive logic reasoning method that firstly extracts query-related (candidate-related) information, and then conducts logic reasoning among the filtered information by inducing feasible rules that entail the target relation. The reasoning process is accomplished via attentive memories with novel differentiable logic operators. To demonstrate the effectiveness of our model, we evaluate it on two reading comprehension datasets, namely WikiHop and MedHop.
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained Models
Authors: Biru Zhu, Yujia Qin, Fanchao Qi, Yangdong Deng, Zhiyuan Liu, Maosong Sun, Ming Gu
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Backdoor Attack, Model Selection

Selecting an appropriate pre-trained model (PTM) for a specific downstream task typically requires significant efforts of fine-tuning. To accelerate this process, researchers propose feature-based model selection (FMS) methods, which assess PTMs’ transferability to a specific task in a fast way without fine-tuning. In this work, we argue that current FMS methods are vulnerable, as the assessment mainly relies on the static features extracted from PTMs. However, such features are derived without training PTMs on downstream tasks, and are not necessarily reliable indicators for the PTM’s transferability. To validate our viewpoints, we design two methods to evaluate the robustness of FMS: (1) model disguise attack, which post-trains an inferior PTM with a contrastive objective, and (2) evaluation data selection, which selects a subset of the data points for FMS evaluation based on K-means clustering. Experimental results prove that both methods can successfully make FMS mistakenly judge the transferability of PTMs. Moreover, we find that these two methods can further be combined with the backdoor attack to misguide the FMS to select poisoned models. To the best of our knowledge, this is the first work to demonstrate the defects of current FMS algorithms and evaluate their potential security risks. By identifying previously unseen risks of FMS, our study indicates new directions for improving the robustness of FMS.
The patient is more dead than alive: exploring the current state of the multi-document summarisation of the biomedical literature
Authors: Yulia Otmakhova, Karin Verspoor, Timothy Baldwin, Jey Han Lau
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5

Although multi-document summarisation (MDS) of the biomedical literature is a highly valuable task that has recently attracted substantial interest, evaluation of the quality of biomedical summaries lacks consistency and transparency. In this paper, we examine the summaries generated by two current models in order to understand the deficiencies of existing evaluation approaches in the context of the challenges that arise in the MDS task. Based on this analysis, we propose a new approach to human evaluation and identify several challenges that must be overcome to develop effective biomedical MDS systems.
KNN-Contrastive Learning for Out-of-Domain Intent Classification
Authors: Yunhua Zhou, Peiju Liu, Xipeng Qiu
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Classification, Contrastive Learning, intent-classification, Intent Classification, Novelty Detection, Out of Distribution (OOD) Detection

The Out-of-Domain (OOD) intent classification is a basic and challenging task for dialogue systems. Previous methods commonly restrict the region (in feature space) of In-domain (IND) intent features to be compact or simply-connected implicitly, which assumes no OOD intents reside, to learn discriminative semantic features. Then the distribution of the IND intent features is often assumed to obey a hypothetical distribution (Gaussian mostly) and samples outside this distribution are regarded as OOD samples. In this paper, we start from the nature of OOD intent classification and explore its optimization objective. We further propose a simple yet effective method, named KNN-contrastive learning. Our approach utilizes k-nearest neighbors (KNN) of IND intents to learn discriminative semantic features that are more conducive to OOD detection.Notably, the density-based novelty detection algorithm is so well-grounded in the essence of our method that it is reasonable to use it as the OOD detection algorithm without making any requirements for the feature distribution.Extensive experiments on four public datasets show that our approach can not only enhance the OOD detection performance substantially but also improve the IND intent classification while requiring no restrictions on feature distribution.
Phone-ing it in: Towards Flexible Multi-Modal Language Model Training by Phonetic Representations of Data
Authors: Colin Leong, Daniel Whitenack
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Multi-modal techniques offer significant untapped potential to unlock improved NLP technology for local languages. However, many advances in language model pre-training are focused on text, a fact that only increases systematic inequalities in the performance of NLP tasks across the world’s languages. In this work, we propose a multi-modal approach to train language models using whatever text and/or audio data might be available in a language. Initial experiments using Swahili and Kinyarwanda data suggest the viability of the approach for downstream Named Entity Recognition (NER) tasks, with models pre-trained on phone data showing an improvement of up to 6% F1-score above models that are trained from scratch. Preprocessing and training code will be uploaded to https://github.com/sil-ai/phone-it-in.
IMPLI: Investigating NLI Models’ Performance on Figurative Language
Authors: Kevin Stowe, Prasetya Utama, Iryna Gurevych
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Natural Language Inference

Natural language inference (NLI) has been widely used as a task to train and evaluate models for language understanding. However, the ability of NLI models to perform inferences requiring understanding of figurative language such as idioms and metaphors remains understudied. We introduce the IMPLI (Idiomatic and Metaphoric Paired Language Inference) dataset, an English dataset consisting of paired sentences spanning idioms and metaphors. We develop novel methods to generate 24k semiautomatic pairs as well as manually creating 1.8k gold pairs. We use IMPLI to evaluate NLI models based on RoBERTa fine-tuned on the widely used MNLI dataset. We then show that while they can reliably detect entailment relationship between figurative phrases with their literal counterparts, they perform poorly on similarly structured examples where pairs are designed to be non-entailing. This suggests the limits of current NLI models with regard to understanding figurative language and this dataset serves as a benchmark for future improvements in this direction.
Semantic Composition with PSHRG for Derivation Tree Reconstruction from Graph-Based Meaning Representations
Authors: Chun Hei Lo, Wai Lam, Hong Cheng
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Semantic Composition

We introduce a data-driven approach to generating derivation trees from meaning representation graphs with probabilistic synchronous hyperedge replacement grammar (PSHRG). SHRG has been used to produce meaning representation graphs from texts and syntax trees, but little is known about its viability on the reverse. In particular, we experiment on Dependency Minimal Recursion Semantics (DMRS) and adapt PSHRG as a formalism that approximates the semantic composition of DMRS graphs and simultaneously recovers the derivations that license the DMRS graphs. Consistent results are obtained as evaluated on a collection of annotated corpora. This work reveals the ability of PSHRG in formalizing a syntax–semantics interface, modelling compositional graph-to-tree translations, and channelling explainability to surface realization.
HOLM: Hallucinating Objects with Language Models for Referring Expression Recognition in Partially-Observed Scenes
Authors: Volkan Cirik, Louis-Philippe Morency, Taylor Berg-Kirkpatrick
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Referring Expression

AI systems embodied in the physical world face a fundamental challenge of partial observability; operating with only a limited view and knowledge of the environment. This creates challenges when AI systems try to reason about language and its relationship with the environment: objects referred to through language (e.g. giving many instructions) are not immediately visible. Actions by the AI system may be required to bring these objects in view. A good benchmark to study this challenge is Dynamic Referring Expression Recognition (dRER) task, where the goal is to find a target location by dynamically adjusting the field of view (FoV) in a partially observed 360 scenes. In this paper, we introduce HOLM, Hallucinating Objects with Language Models, to address the challenge of partial observability. HOLM uses large pre-trained language models (LMs) to infer object hallucinations for the unobserved part of the environment. Our core intuition is that if a pair of objects co-appear in an environment frequently, our usage of language should reflect this fact about the world. Based on this intuition, we prompt language models to extract knowledge about object affinities which gives us a proxy for spatial relationships of objects. Our experiments show that HOLM performs better than the state-of-the-art approaches on two datasets for dRER; allowing to study generalization for both indoor and outdoor settings.
\infty-former: Infinite Memory Transformer
Authors: Pedro Henrique Martins, Zita Marinho, Andre Martins
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Dialogue Generation, Language Modeling, Language Modelling

Transformers are unable to model long-term memories effectively, since the amount of computation they need to perform grows with the context length. While variations of efficient transformers have been proposed, they all have a finite memory capacity and are forced to drop old information. In this paper, we propose the \infty-former, which extends the vanilla transformer with an unbounded long-term memory. By making use of a continuous-space attention mechanism to attend over the long-term memory, the \infty-former’s attention complexity becomes independent of the context length, trading off memory length with precision.In order to control where precision is more important, \infty-former maintains “sticky memories,” being able to model arbitrarily long contexts while keeping the computation budget fixed.Experiments on a synthetic sorting task, language modeling, and document grounded dialogue generation demonstrate the \infty-former’s ability to retain information from long sequences.
Systematic Inequalities in Language Technology Performance across the World’s Languages
Authors: Damian Blasi, Antonios Anastasopoulos, Graham Neubig
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Dependency Parsing, Machine Translation, Morphological Inflection, Question Answering, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Natural language processing (NLP) systems have become a central technology in communication, education, medicine, artificial intelligence, and many other domains of research and development. While the performance of NLP methods has grown enormously over the last decade, this progress has been restricted to a minuscule subset of the world’s \approx6,500 languages. We introduce a framework for estimating the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. Our analyses involve the field at large, but also more in-depth studies on both user-facing technologies (machine translation, language understanding, question answering, text-to-speech synthesis) as well as foundational NLP tasks (dependency parsing, morphological inflection). In the process, we (1) quantify disparities in the current state of NLP research, (2) explore some of its associated societal and academic factors, and (3) produce tailored recommendations for evidence-based policy making aimed at promoting more global and equitable language technologies. Data and code to reproduce the findings discussed in this paper areavailable on GitHub (https://github.com/neubig/globalutility).
Reports of personal experiences and stories in argumentation: datasets and analysis
Authors: Neele Falk, Gabriella Lapesa
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Argument Mining, domain classification

Reports of personal experiences or stories can play a crucial role in argumentation, as they represent an immediate and (often) relatable way to back up one’s position with respect to a given topic. They are easy to understand and increase empathy: this makes them powerful in argumentation. The impact of personal reports and stories in argumentation has been studied in the Social Sciences, but it is still largely underexplored in NLP. Our work is the first step towards filling this gap: our goal is to develop robust classifiers to identify documents containing personal experiences and reports. The main challenge is the scarcity of annotated data: our solution is to leverage existing annotations to be able to scale-up the analysis. Our contribution is two-fold. First, we conduct a set of in-domain and cross-domain experiments involving three datasets (two from Argument Mining, one from the Social Sciences), modeling architectures, training setups and fine-tuning options tailored to the involved domains. We show that despite the differences among datasets and annotations, robust cross-domain classification is possible. Second, we employ linear regression for performance mining, identifying performance trends both for overall classification performance and individual classifier predictions.