Transactions of the Association for Computational Linguistics最新文献_第2页

An Efficient Self-Supervised Cross-View Training For Sentence Embedding 用于句子嵌入的高效自监督交叉视图训练

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-11-06 DOI: 10.1162/tacl_a_00620

Peerat Limkonchotiwat, Wuttikorn Ponwitayarat, Lalita Lowphansirikul, Can Udomcharoenchaikit, E. Chuangsuwanich, Sarana Nutanong

引用次数: 0

U-CORE: A Unified Deep Cluster-wise Contrastive Framework for Open Relation Extraction U-CORE：用于开放关系提取的统一深度聚类对比框架

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-11-01 DOI: 10.1162/tacl_a_00604

Jie Zhou, Shenpo Dong, Yunxin Huang, Meihan Wu, Haili Li, Jingnan Wang, Hongkui Tu, Xiaodong Wang

{"title":"U-CORE: A Unified Deep Cluster-wise Contrastive Framework for Open Relation Extraction","authors":"Jie Zhou, Shenpo Dong, Yunxin Huang, Meihan Wu, Haili Li, Jingnan Wang, Hongkui Tu, Xiaodong Wang","doi":"10.1162/tacl_a_00604","DOIUrl":"https://doi.org/10.1162/tacl_a_00604","url":null,"abstract":"Abstract Within Open Relation Extraction (ORE) tasks, the Zero-shot ORE method is to generalize undefined relations from predefined relations, while the Unsupervised ORE method is to extract undefined relations without the need for annotations. However, despite the possibility of overlap between predefined and undefined relations in the training data, a unified framework for both Zero-shot and Unsupervised ORE has yet to be established. To address this gap, we propose U-CORE: A Unified Deep Cluster-wise Contrastive Framework for both Zero-shot and Unsupervised ORE, by leveraging techniques from Contrastive Learning (CL) and Clustering.1 U-CORE overcomes the limitations of CL-based Zero-shot ORE methods by employing Cluster-wise CL that preserves both local smoothness as well as global semantics. Additionally, we employ a deep-cluster-based updater that optimizes the cluster center, thus enhancing the accuracy and efficiency of the model. To increase the stability of the model, we adopt Adaptive Self-paced Learning that effectively addresses the data-shifting problems. Experimental results on three well-known datasets demonstrate that U-CORE significantly improves upon existing methods by showing an average improvement of 7.35% ARI on Zero-shot ORE tasks and 15.24% ARI on Unsupervised ORE tasks.","PeriodicalId":33559,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":null,"pages":null},"PeriodicalIF":10.9,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139297367","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

AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR AfriSpeech-200：用于临床和通用领域 ASR 的泛非洲重音语音数据集

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-09-30 DOI: 10.1162/tacl_a_00627

Tobi Olatunji, Tejumade Afonja, Aditya Yadavalli, C. Emezue, Sahib Singh, Bonaventure F. P. Dossou, Joanne Osuchukwu, Salomey Osei, A. Tonja, Naome A. Etori, Clinton Mbataku

{"title":"AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR","authors":"Tobi Olatunji, Tejumade Afonja, Aditya Yadavalli, C. Emezue, Sahib Singh, Bonaventure F. P. Dossou, Joanne Osuchukwu, Salomey Osei, A. Tonja, Naome A. Etori, Clinton Mbataku","doi":"10.1162/tacl_a_00627","DOIUrl":"https://doi.org/10.1162/tacl_a_00627","url":null,"abstract":"Abstract Africa has a very poor doctor-to-patient ratio. At very busy clinics, doctors could see 30+ patients per day—a heavy patient burden compared with developed countries—but productivity tools such as clinical automatic speech recognition (ASR) are lacking for these overworked clinicians. However, clinical ASR is mature, even ubiquitous, in developed nations, and clinician-reported performance of commercial clinical ASR systems is generally satisfactory. Furthermore, the recent performance of general domain ASR is approaching human accuracy. However, several gaps exist. Several publications have highlighted racial bias with speech-to-text algorithms and performance on minority accents lags significantly. To our knowledge, there is no publicly available research or benchmark on accented African clinical ASR, and speech data is non-existent for the majority of African accents. We release AfriSpeech, 200hrs of Pan-African English speech, 67,577 clips from 2,463 unique speakers across 120 indigenous accents from 13 countries for clinical and general domain ASR, a benchmark test set, with publicly available pre-trained models with SOTA performance on the AfriSpeech benchmark.","PeriodicalId":33559,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":null,"pages":null},"PeriodicalIF":10.9,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139332019","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

MIRACL: A Multilingual Retrieval Dataset Covering 18 Diverse Languages MIRACL:一个涵盖18种不同语言的多语言检索数据集

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-09-01 DOI: 10.1162/tacl_a_00595

Xinyu Crystina Zhang, Nandan Thakur, Odunayo Ogundepo, Ehsan Kamalloo, David Alfonso-Hermelo, Xiaoguang Li, Qun Liu, Mehdi Rezagholizadeh, Jimmy Lin

{"title":"MIRACL: A Multilingual Retrieval Dataset Covering 18 Diverse Languages","authors":"Xinyu Crystina Zhang, Nandan Thakur, Odunayo Ogundepo, Ehsan Kamalloo, David Alfonso-Hermelo, Xiaoguang Li, Qun Liu, Mehdi Rezagholizadeh, Jimmy Lin","doi":"10.1162/tacl_a_00595","DOIUrl":"https://doi.org/10.1162/tacl_a_00595","url":null,"abstract":"Abstract MIRACL is a multilingual dataset for ad hoc retrieval across 18 languages that collectively encompass over three billion native speakers around the world. This resource is designed to support monolingual retrieval tasks, where the queries and the corpora are in the same language. In total, we have gathered over 726k high-quality relevance judgments for 78k queries over Wikipedia in these languages, where all annotations have been performed by native speakers hired by our team. MIRACL covers languages that are both typologically close as well as distant from 10 language families and 13 sub-families, associated with varying amounts of publicly available resources. Extensive automatic heuristic verification and manual assessments were performed during the annotation process to control data quality. In total, MIRACL represents an investment of around five person-years of human annotator effort. Our goal is to spur research on improving retrieval across a continuum of languages, thus enhancing information access capabilities for diverse populations around the world, particularly those that have traditionally been underserved. MIRACL is available at http://miracl.ai/.","PeriodicalId":33559,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":null,"pages":null},"PeriodicalIF":10.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64440768","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

Shared Lexical Items as Triggers of Code Switching 共享词条是代码转换的触发器

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-08-29 DOI: 10.1162/tacl_a_00613

S. Wintner, Safaa Shehadi, Yuli Zeira, Doreen Osmelak, Yuval Nov

引用次数: 0

Can Authorship Representation Learning Capture Stylistic Features? 作者表征学习能否捕捉文体特征？

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-08-22 DOI: 10.1162/tacl_a_00610

Andrew Wang, Cristina Aggazzotti, R. Kotula, Rafael A. Rivera Soto, M. Bishop, Nicholas Andrews

{"title":"Can Authorship Representation Learning Capture Stylistic Features?","authors":"Andrew Wang, Cristina Aggazzotti, R. Kotula, Rafael A. Rivera Soto, M. Bishop, Nicholas Andrews","doi":"10.1162/tacl_a_00610","DOIUrl":"https://doi.org/10.1162/tacl_a_00610","url":null,"abstract":"Abstract Automatically disentangling an author’s style from the content of their writing is a longstanding and possibly insurmountable problem in computational linguistics. At the same time, the availability of large text corpora furnished with author labels has recently enabled learning authorship representations in a purely data-driven manner for authorship attribution, a task that ostensibly depends to a greater extent on encoding writing style than encoding content. However, success on this surrogate task does not ensure that such representations capture writing style since authorship could also be correlated with other latent variables, such as topic. In an effort to better understand the nature of the information these representations convey, and specifically to validate the hypothesis that they chiefly encode writing style, we systematically probe these representations through a series of targeted experiments. The results of these experiments suggest that representations learned for the surrogate authorship prediction task are indeed sensitive to writing style. As a consequence, authorship representations may be expected to be robust to certain kinds of data shift, such as topic drift over time. Additionally, our findings may open the door to downstream applications that require stylistic representations, such as style transfer.","PeriodicalId":33559,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":null,"pages":null},"PeriodicalIF":10.9,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139349572","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

PaniniQA: Enhancing Patient Education Through Interactive Question Answering PaniniQA：通过交互式问题解答加强患者教育

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-08-07 DOI: 10.1162/tacl_a_00616

Pengshan Cai, Zonghai Yao, Fei Liu, Dakuo Wang, Meghan Reilly, Huixue Zhou, Lingxi Li, Yifan Cao, Alok Kapoor, Adarsha S. Bajracharya, D. Berlowitz, Hongfeng Yu

引用次数: 0

Learning to Paraphrase Sentences to Different Complexity Levels 学习仿写不同复杂程度的句子

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-08-04 DOI: 10.1162/tacl_a_00606

Alison Chi, Li-Kuang Chen, Yi-Chen Chang, Shu-Hui Lee, Jason J. S. Chang

引用次数: 0

Collective Human Opinions in Semantic Textual Similarity 语义文本相似度中的人类集体意见

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-08-01 DOI: 10.1162/tacl_a_00584

Yuxia Wang, Shimin Tao, Ning Xie, Hao Yang, Timothy Baldwin, K. Verspoor

引用次数: 2

Time-and-Space-Efficient Weighted Deduction 时间和空间效率加权扣除

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-08-01 DOI: 10.1162/tacl_a_00588

Jason Eisner

引用次数: 1