Transactions of the Association for Computational Linguistics最新文献_第4页

Compositional Zero-Shot Domain Transfer with Text-to-Text Models 文本到文本模型的组合零射击域转移

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-03-23 DOI: 10.1162/tacl_a_00585

Fangyu Liu, Qianchu Liu, Shruthi Bannur, Fernando Pérez-García, Naoto Usuyama, Shenmin Zhang, Tristan Naumann, A. Nori, Hoifung Poon, J. Alvarez-Valle, O. Oktay, Stephanie L. Hyland

{"title":"Compositional Zero-Shot Domain Transfer with Text-to-Text Models","authors":"Fangyu Liu, Qianchu Liu, Shruthi Bannur, Fernando Pérez-García, Naoto Usuyama, Shenmin Zhang, Tristan Naumann, A. Nori, Hoifung Poon, J. Alvarez-Valle, O. Oktay, Stephanie L. Hyland","doi":"10.1162/tacl_a_00585","DOIUrl":"https://doi.org/10.1162/tacl_a_00585","url":null,"abstract":"Abstract Label scarcity is a bottleneck for improving task performance in specialized domains. We propose a novel compositional transfer learning framework (DoT51) for zero-shot domain transfer. Without access to in-domain labels, DoT5 jointly learns domain knowledge (from masked language modelling of unlabelled in-domain free text) and task knowledge (from task training on more readily available general-domain data) in a multi-task manner. To improve the transferability of task training, we design a strategy named NLGU: We simultaneously train natural language generation (NLG) for in-domain label-to-data generation, which enables data augmentation for self-finetuning and natural language understanding (NLU) for label prediction. We evaluate DoT5 on the biomedical domain and the resource-lean subdomain of radiology, focusing on natural language inference, text summarization, and embedding learning. DoT5 demonstrates the effectiveness of compositional transfer learning through multi-task learning. In particular, DoT5 outperforms the current state-of-the-art in zero-shot transfer by over 7 absolute points in accuracy on RadNLI. We validate DoT5 with ablations and a case study demonstrating its ability to solve challenging NLI examples requiring in-domain expertise.","PeriodicalId":33559,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":"11 1","pages":"1097-1113"},"PeriodicalIF":10.9,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43977430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Evaluating Transformer Models and Human Behaviors on Chinese Character Naming 评价变形模型与汉字命名中的人类行为

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-03-22 DOI: 10.1162/tacl_a_00573

Xiaomeng Ma, Lingyu Gao

引用次数: 0

Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery 少即是多:通过因果发现减轻开放域对话响应生成模型的虚假关联

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-03-02 DOI: 10.1162/tacl_a_00561

Tao Feng, Lizhen Qu, Gholamreza Haffari

引用次数: 0

FeelingBlue: A Corpus for Understanding the Emotional Connotation of Color in Context 感受蓝色:理解语境中色彩情感内涵的语料库

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-03-01 DOI: 10.1162/tacl_a_00540

Amith Ananthram, Olivia Winn, S. Muresan

{"title":"FeelingBlue: A Corpus for Understanding the Emotional Connotation of Color in Context","authors":"Amith Ananthram, Olivia Winn, S. Muresan","doi":"10.1162/tacl_a_00540","DOIUrl":"https://doi.org/10.1162/tacl_a_00540","url":null,"abstract":"While the link between color and emotion has been widely studied, how context-based changes in color impact the intensity of perceived emotions is not well understood. In this work, we present a new multimodal dataset for exploring the emotional connotation of color as mediated by line, stroke, texture, shape, and language. Our dataset, FeelingBlue, is a collection of 19,788 4-tuples of abstract art ranked by annotators according to their evoked emotions and paired with rationales for those annotations. Using this corpus, we present a baseline for a new task: Justified Affect Transformation. Given an image I, the task is to 1) recolor I to enhance a specified emotion e and 2) provide a textual justification for the change in e. Our model is an ensemble of deep neural networks which takes I, generates an emotionally transformed color palette p conditioned on I, applies p to I, and then justifies the color transformation in text via a visual-linguistic model. Experimental results shed light on the emotional connotation of color in context, demonstrating both the promise of our approach on this challenging task and the considerable potential for future investigations enabled by our corpus.1","PeriodicalId":33559,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":"11 1","pages":"176-190"},"PeriodicalIF":10.9,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44321137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transformers for Tabular Data Representation: A Survey of Models and Applications 表格数据表示的变换器：模型和应用综述

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-03-01 DOI: 10.1162/tacl_a_00544

Gilbert Badaro, Mohammed Saeed, Paolo Papotti

引用次数: 13

Hate Speech Classifiers Learn Normative Social Stereotypes 仇恨言语分类器学习规范的社会刻板印象

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-03-01 DOI: 10.1162/tacl_a_00550

A. Davani, M. Atari, Brendan Kennedy, Morteza Dehghani

{"title":"Hate Speech Classifiers Learn Normative Social Stereotypes","authors":"A. Davani, M. Atari, Brendan Kennedy, Morteza Dehghani","doi":"10.1162/tacl_a_00550","DOIUrl":"https://doi.org/10.1162/tacl_a_00550","url":null,"abstract":"Social stereotypes negatively impact individuals’ judgments about different groups and may have a critical role in understanding language directed toward marginalized groups. Here, we assess the role of social stereotypes in the automated detection of hate speech in the English language by examining the impact of social stereotypes on annotation behaviors, annotated datasets, and hate speech classifiers. Specifically, we first investigate the impact of novice annotators’ stereotypes on their hate-speech-annotation behavior. Then, we examine the effect of normative stereotypes in language on the aggregated annotators’ judgments in a large annotated corpus. Finally, we demonstrate how normative stereotypes embedded in language resources are associated with systematic prediction errors in a hate-speech classifier. The results demonstrate that hate-speech classifiers reflect social stereotypes against marginalized groups, which can perpetuate social inequalities when propagated at scale. This framework, combining social-psychological and computational-linguistic methods, provides insights into sources of bias in hate-speech moderation, informing ongoing debates regarding machine learning fairness.","PeriodicalId":33559,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":"11 1","pages":"300-319"},"PeriodicalIF":10.9,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46301957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Discontinuous Combinatory Constituency Parsing 不连续组合选区解析

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-03-01 DOI: 10.1162/tacl_a_00546

Zhousi Chen, Mamoru Komachi

引用次数: 0

On Graph-based Reentrancy-free Semantic Parsing 基于图的无重入语义分析

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-02-15 DOI: 10.1162/tacl_a_00570

Alban Petit, Caio Corro

引用次数: 2

Erasure of Unaligned Attributes from Neural Representations 从神经表示中删除未对齐的属性

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-02-06 DOI: 10.1162/tacl_a_00558

Shun Shao, Yftah Ziser, Shay B. Cohen

引用次数: 5

Unleashing the True Potential of Sequence-to-Sequence Models for Sequence Tagging and Structure Parsing 释放序列到序列模型的真正潜力，用于序列标记和结构解析

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-02-05 DOI: 10.1162/tacl_a_00557

Han He, Jinho D. Choi

{"title":"Unleashing the True Potential of Sequence-to-Sequence Models for Sequence Tagging and Structure Parsing","authors":"Han He, Jinho D. Choi","doi":"10.1162/tacl_a_00557","DOIUrl":"https://doi.org/10.1162/tacl_a_00557","url":null,"abstract":"Sequence-to-Sequence (S2S) models have achieved remarkable success on various text generation tasks. However, learning complex structures with S2S models remains challenging as external neural modules and additional lexicons are often supplemented to predict non-textual outputs. We present a systematic study of S2S modeling using contained decoding on four core tasks: part-of-speech tagging, named entity recognition, constituency, and dependency parsing, to develop efficient exploitation methods costing zero extra parameters. In particular, 3 lexically diverse linearization schemas and corresponding constrained decoding methods are designed and evaluated. Experiments show that although more lexicalized schemas yield longer output sequences that require heavier training, their sequences being closer to natural language makes them easier to learn. Moreover, S2S models using our constrained decoding outperform other S2S approaches using external resources. Our best models perform better than or comparably to the state-of-the-art for all 4 tasks, lighting a promise for S2S models to generate non-sequential structures.","PeriodicalId":33559,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":"11 1","pages":"582-599"},"PeriodicalIF":10.9,"publicationDate":"2023-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42828943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0