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

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

Modeling Emotion Dynamics in Song Lyrics with State Space Models 用状态空间模型建模歌词中的情感动态

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-02-01 DOI: 10.1162/tacl_a_00541

Yingjin Song, Daniel Beck

引用次数: 0

Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-off 交流驱动神经主体语言共性的出现:来自语序/区分大小写权衡的证据

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-01-30 DOI: 10.1162/tacl_a_00587

Yuchen Lian, Arianna Bisazza, T. Verhoef

{"title":"Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-off","authors":"Yuchen Lian, Arianna Bisazza, T. Verhoef","doi":"10.1162/tacl_a_00587","DOIUrl":"https://doi.org/10.1162/tacl_a_00587","url":null,"abstract":"Abstract Artificial learners often behave differently from human learners in the context of neural agent-based simulations of language emergence and change. A common explanation is the lack of appropriate cognitive biases in these learners. However, it has also been proposed that more naturalistic settings of language learning and use could lead to more human-like results. We investigate this latter account, focusing on the word-order/case-marking trade-off, a widely attested language universal that has proven particularly hard to simulate. We propose a new Neural-agent Language Learning and Communication framework (NeLLCom) where pairs of speaking and listening agents first learn a miniature language via supervised learning, and then optimize it for communication via reinforcement learning. Following closely the setup of earlier human experiments, we succeed in replicating the trade-off with the new framework without hard-coding specific biases in the agents. We see this as an essential step towards the investigation of language universals with neural learners.","PeriodicalId":33559,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":"11 1","pages":"1033-1047"},"PeriodicalIF":10.9,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43044930","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

Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences 视觉写作提示:以人物为基础的故事生成与策划图像序列

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-01-20 DOI: 10.1162/tacl_a_00553

Xudong Hong, A. Sayeed, K. Mehra, Vera Demberg, B. Schiele

引用次数: 8