Transactions of the Association for Computational Linguistics最新文献_第5页

Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection 基于模型自省的神经机器翻译中幻觉的理解与检测

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-01-18 DOI: 10.1162/tacl_a_00563

Weijia Xu, Sweta Agrawal, Eleftheria Briakou, Marianna J. Martindale, Marine Carpuat

引用次数: 13

Tracking Brand-Associated Polarity-Bearing Topics in User Reviews 追踪用户评论中与品牌相关的极性话题

IF 10.9 1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-01-03 DOI: 10.1162/tacl_a_00555

Runcong Zhao, Lin Gui, Hanqi Yan, Yulan He

引用次数: 0

T 2 -NER: A Two-Stage Span-Based Framework for Unified Named Entity Recognition with Templates t2 -NER:一种基于两阶段跨度的模板统一命名实体识别框架

1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-01-01 DOI: 10.1162/tacl_a_00602

Peixin Huang, Xiang Zhao, Minghao Hu, Zhen Tan, Weidong Xiao

{"title":"T 2 -NER: A Two-Stage Span-Based Framework for Unified Named Entity Recognition with Templates","authors":"Peixin Huang, Xiang Zhao, Minghao Hu, Zhen Tan, Weidong Xiao","doi":"10.1162/tacl_a_00602","DOIUrl":"https://doi.org/10.1162/tacl_a_00602","url":null,"abstract":"Abstract Named Entity Recognition (NER) has so far evolved from the traditional flat NER to overlapped and discontinuous NER. They have mostly been solved separately, with only several exceptions that concurrently tackle three tasks with a single model. The current best-performing method formalizes the unified NER as word-word relation classification, which barely focuses on mention content learning and fails to detect entity mentions comprising a single word. In this paper, we propose a two-stage span-based framework with templates, namely, T2-NER, to resolve the unified NER task. The first stage is to extract entity spans, where flat and overlapped entities can be recognized. The second stage is to classify over all entity span pairs, where discontinuous entities can be recognized. Finally, multi-task learning is used to jointly train two stages. To improve the efficiency of span-based model, we design grouped templates and typed templates for two stages to realize batch computations. We also apply an adjacent packing strategy and a latter packing strategy to model discriminative boundary information and learn better span (pair) representation. Moreover, we introduce the syntax information to enhance our span representation. We perform extensive experiments on eight benchmark datasets for flat, overlapped, and discontinuous NER, where our model beats all the current competitive baselines, obtaining the best performance of unified NER.","PeriodicalId":33559,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":"5 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":"135057751","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

PASTA: A Dataset for Modeling PArticipant STAtes in Narratives PASTA:叙事中参与者状态建模的数据集

1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-01-01 DOI: 10.1162/tacl_a_00600

Sayontan Ghosh, Mahnaz Koupaee, Isabella Chen, Francis Ferraro, Nathanael Chambers, Niranjan Balasubramanian

{"title":"<tt>PASTA</tt>: A Dataset for Modeling PArticipant STAtes in Narratives","authors":"Sayontan Ghosh, Mahnaz Koupaee, Isabella Chen, Francis Ferraro, Nathanael Chambers, Niranjan Balasubramanian","doi":"10.1162/tacl_a_00600","DOIUrl":"https://doi.org/10.1162/tacl_a_00600","url":null,"abstract":"Abstract The events in a narrative are understood as a coherent whole via the underlying states of their participants. Often, these participant states are not explicitly mentioned, instead left to be inferred by the reader. A model that understands narratives should likewise infer these implicit states, and even reason about the impact of changes to these states on the narrative. To facilitate this goal, we introduce a new crowdsourced English-language, Participant States dataset, PASTA. This dataset contains inferable participant states; a counterfactual perturbation to each state; and the changes to the story that would be necessary if the counterfactual were true. We introduce three state-based reasoning tasks that test for the ability to infer when a state is entailed by a story, to revise a story conditioned on a counterfactual state, and to explain the most likely state change given a revised story. Experiments show that today’s LLMs can reason about states to some degree, but there is large room for improvement, especially in problems requiring access and ability to reason with diverse types of knowledge (e.g., physical, numerical, factual).1","PeriodicalId":33559,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":"21 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":"135560520","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: 2

Calibrated Interpretation: Confidence Estimation in Semantic Parsing 校正解释:语义解析中的置信度估计

1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-01-01 DOI: 10.1162/tacl_a_00598

Elias Stengel-Eskin, Benjamin Van Durme

引用次数: 6

Improving Multitask Retrieval by Promoting Task Specialization 通过促进任务专门化改进多任务检索

1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-01-01 DOI: 10.1162/tacl_a_00597

Wenzheng Zhang, Chenyan Xiong, Karl Stratos, Arnold Overwijk

引用次数: 0

Benchmarking the Generation of Fact Checking Explanations 对事实核查解释的生成进行基准测试

1区计算机科学

Transactions of the Association for Computational Linguistics Pub Date : 2023-01-01 DOI: 10.1162/tacl_a_00601

Daniel Russo, Serra Sinem Tekiroğlu, Marco Guerini

{"title":"Benchmarking the Generation of Fact Checking Explanations","authors":"Daniel Russo, Serra Sinem Tekiroğlu, Marco Guerini","doi":"10.1162/tacl_a_00601","DOIUrl":"https://doi.org/10.1162/tacl_a_00601","url":null,"abstract":"Abstract Fighting misinformation is a challenging, yet crucial, task. Despite the growing number of experts being involved in manual fact-checking, this activity is time-consuming and cannot keep up with the ever-increasing amount of fake news produced daily. Hence, automating this process is necessary to help curb misinformation. Thus far, researchers have mainly focused on claim veracity classification. In this paper, instead, we address the generation of justifications (textual explanation of why a claim is classified as either true or false) and benchmark it with novel datasets and advanced baselines. In particular, we focus on summarization approaches over unstructured knowledge (i.e., news articles) and we experiment with several extractive and abstractive strategies. We employed two datasets with different styles and structures, in order to assess the generalizability of our findings. Results show that in justification production summarization benefits from the claim information, and, in particular, that a claim-driven extractive step improves abstractive summarization performances. Finally, we show that although cross-dataset experiments suffer from performance degradation, a unique model trained on a combination of the two datasets is able to retain style information in an efficient manner.","PeriodicalId":33559,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":"6 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":"135057750","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 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