Transactions of the Association for Computational Linguistics最新文献_第6页

Evaluating a Century of Progress on the Cognitive Science of Adjective Ordering 评价一个世纪以来形容词排序认知科学的进展

1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-01-01 DOI: 10.1162/tacl_a_00596

William Dyer, Charles Torres, Gregory Scontras, Richard Futrell

引用次数: 0

Introduction to Mathematical Language Processing: Informal Proofs, Word Problems, and Supporting Tasks 数学语言处理导论:非正式证明、文字问题和辅助任务

1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-01-01 DOI: 10.1162/tacl_a_00594

Jordan Meadows, André Freitas

引用次数: 1

T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification T3L:跨语言文本分类的翻译-测试迁移学习

1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-01-01 DOI: 10.1162/tacl_a_00593

Inigo Jauregi Unanue, Gholamreza Haffari, Massimo Piccardi

{"title":"T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification","authors":"Inigo Jauregi Unanue, Gholamreza Haffari, Massimo Piccardi","doi":"10.1162/tacl_a_00593","DOIUrl":"https://doi.org/10.1162/tacl_a_00593","url":null,"abstract":"Abstract Cross-lingual text classification leverages text classifiers trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning (zero/ few-shots cross-lingual transfer). Nowadays, cross-lingual text classifiers are typically built on large-scale, multilingual language models (LMs) pretrained on a variety of languages of interest. However, the performance of these models varies significantly across languages and classification tasks, suggesting that the superposition of the language modelling and classification tasks is not always effective. For this reason, in this paper we propose revisiting the classic “translate-and-test” pipeline to neatly separate the translation and classification stages. The proposed approach couples 1) a neural machine translator translating from the targeted language to a high-resource language, with 2) a text classifier trained in the high-resource language, but the neural machine translator generates “soft” translations to permit end-to-end backpropagation during fine-tuning of the pipeline. Extensive experiments have been carried out over three cross-lingual text classification datasets (XNLI, MLDoc, and MultiEURLEX), with the results showing that the proposed approach has significantly improved performance over a competitive baseline.","PeriodicalId":33559,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135596945","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

DMDD: A Large-Scale Dataset for Dataset Mentions Detection DMDD:用于数据集提及检测的大规模数据集

1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-01-01 DOI: 10.1162/tacl_a_00592

Huitong Pan, Qi Zhang, Eduard Dragut, Cornelia Caragea, Longin Jan Latecki

引用次数: 2

Dubbing in Practice: A Large Scale Study of Human Localization With Insights for Automatic Dubbing 实践中的配音：一个大规模的人类本地化研究——对自动配音的见解

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2022-12-23 DOI: 10.1162/tacl_a_00551

William Brannon, Yogesh Virkar, Brian Thompson

引用次数: 8

Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times? 为什么更大的基于变压器的语言模型提供的惊喜更不适合人类的阅读时间?

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2022-12-23 DOI: 10.1162/tacl_a_00548

Byung-Doh Oh, William Schuler

引用次数: 30

Assessing the Capacity of Transformer to Abstract Syntactic Representations: A Contrastive Analysis Based on Long-distance Agreement 变压器抽象句法表征能力的评估——基于长距离一致性的对比分析

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2022-12-08 DOI: 10.1162/tacl_a_00531

Bingzhi Li, Guillaume Wisniewski, Benoit Crabb'e

引用次数: 3

The Emergence of Argument Structure in Artificial Languages 论人工语言中论证结构的产生

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2022-12-01 DOI: 10.1162/tacl_a_00524

Tom Bosc, Pascal Vincent

{"title":"The Emergence of Argument Structure in Artificial Languages","authors":"Tom Bosc, Pascal Vincent","doi":"10.1162/tacl_a_00524","DOIUrl":"https://doi.org/10.1162/tacl_a_00524","url":null,"abstract":"Abstract Computational approaches to the study of language emergence can help us understand how natural languages are shaped by cognitive and sociocultural factors. Previous work focused on tasks where agents refer to a single entity. In contrast, we study how agents predicate, that is, how they express that some relation holds between several entities. We introduce a setup where agents talk about a variable number of entities that can be partially observed by the listener. In the presence of a least-effort pressure, they tend to discuss only entities that are not observed by the listener. Thus we can obtain artificial phrases that denote a single entity, as well as artificial sentences that denote several entities. In natural languages, if we ignore the verb, phrases are usually concatenated, either in a specific order or by adding case markers to form sentences. Our setup allows us to quantify how much this holds in emergent languages using a metric we call concatenability. We also measure transitivity, which quantifies the importance of word order. We demonstrate the usefulness of this new setup and metrics for studying factors that influence argument structure. We compare agents having access to input representations structured into pre-segmented objects with properties, versus unstructured representations. Our results indicate that the awareness of object structure yields a more natural sentence organization.","PeriodicalId":33559,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":"10 1","pages":"1375-1391"},"PeriodicalIF":10.9,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42504304","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

Coreference Resolution through a seq2seq Transition-Based System 基于seq2seq转换系统的共参解析

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2022-11-22 DOI: 10.1162/tacl_a_00543

Bernd Bohnet, Chris Alberti, Michael Collins

引用次数: 6

MACSum: Controllable Summarization with Mixed Attributes MACSum:混合属性的可控摘要

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2022-11-09 DOI: 10.1162/tacl_a_00575

Yusen Zhang, Yang Liu, Ziyi Yang, Yuwei Fang, Yulong Chen, Dragomir R. Radev, Chenguang Zhu, Michael Zeng, Rui Zhang

{"title":"MACSum: Controllable Summarization with Mixed Attributes","authors":"Yusen Zhang, Yang Liu, Ziyi Yang, Yuwei Fang, Yulong Chen, Dragomir R. Radev, Chenguang Zhu, Michael Zeng, Rui Zhang","doi":"10.1162/tacl_a_00575","DOIUrl":"https://doi.org/10.1162/tacl_a_00575","url":null,"abstract":"Abstract Controllable summarization allows users to generate customized summaries with specified attributes. However, due to the lack of designated annotations of controlled summaries, existing work has to craft pseudo datasets by adapting generic summarization benchmarks. Furthermore, most research focuses on controlling single attributes individually (e.g., a short summary or a highly abstractive summary) rather than controlling a mix of attributes together (e.g., a short and highly abstractive summary). In this paper, we propose MACSum, the first human-annotated summarization dataset for controlling mixed attributes. It contains source texts from two domains, news articles and dialogues, with human-annotated summaries controlled by five designed attributes (Length, Extractiveness, Specificity, Topic, and Speaker). We propose two simple and effective parameter-efficient approaches for the new task of mixed controllable summarization based on hard prompt tuning and soft prefix tuning. Results and analysis demonstrate that hard prompt models yield the best performance on most metrics and human evaluations. However, mixed-attribute control is still challenging for summarization tasks. Our dataset and code are available at https://github.com/psunlpgroup/MACSum.","PeriodicalId":33559,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":"11 1","pages":"787-803"},"PeriodicalIF":10.9,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46946847","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: 6