Transactions of the Association for Computational Linguistics最新文献

筛选
英文 中文
Beyond Boundaries: A Human-like Approach for Question Answering over Structured and Unstructured Information Sources 超越界限:结构化和非结构化信息源问题解答的类人方法
Transactions of the Association for Computational Linguistics Pub Date : 2024-06-01 DOI: 10.1162/tacl_a_00671
Jens Lehmann, Dhananjay Bhandiwad, Preetam Gattogi, S. Vahdati
{"title":"Beyond Boundaries: A Human-like Approach for Question Answering over Structured and Unstructured Information Sources","authors":"Jens Lehmann, Dhananjay Bhandiwad, Preetam Gattogi, S. Vahdati","doi":"10.1162/tacl_a_00671","DOIUrl":"https://doi.org/10.1162/tacl_a_00671","url":null,"abstract":"Abstract Answering factual questions from heterogenous sources, such as graphs and text, is a key capacity of intelligent systems. Current approaches either (i) perform question answering over text and structured sources as separate pipelines followed by a merge step or (ii) provide an early integration, giving up the strengths of particular information sources. To solve this problem, we present “HumanIQ”, a method that teaches language models to dynamically combine retrieved information by imitating how humans use retrieval tools. Our approach couples a generic method for gathering human demonstrations of tool use with adaptive few-shot learning for tool augmented models. We show that HumanIQ confers significant benefits, including i) reducing the error rate of our strongest baseline (GPT-4) by over 50% across 3 benchmarks, (ii) improving human preference over responses from vanilla GPT-4 (45.3% wins, 46.7% ties, 8.0% loss), and (iii) outperforming numerous task-specific baselines.","PeriodicalId":506323,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":"14 4","pages":"786-802"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141411182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation 基于能量的计算机辅助翻译单词级自动补全模型
Transactions of the Association for Computational Linguistics Pub Date : 2024-02-01 DOI: 10.1162/tacl_a_00637
Cheng Yang, Guoping Huang, Mo Yu, Zhirui Zhang, Siheng Li, Mingming Yang, Shuming Shi, Yujiu Yang, Lemao Liu
{"title":"An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation","authors":"Cheng Yang, Guoping Huang, Mo Yu, Zhirui Zhang, Siheng Li, Mingming Yang, Shuming Shi, Yujiu Yang, Lemao Liu","doi":"10.1162/tacl_a_00637","DOIUrl":"https://doi.org/10.1162/tacl_a_00637","url":null,"abstract":"Abstract Word-level AutoCompletion (WLAC) is a rewarding yet challenging task in Computer-aided Translation. Existing work addresses this task through a classification model based on a neural network that maps the hidden vector of the input context into its corresponding label (i.e., the candidate target word is treated as a label). Since the context hidden vector itself does not take the label into account and it is projected to the label through a linear classifier, the model cannot sufficiently leverage valuable information from the source sentence as verified in our experiments, which eventually hinders its overall performance. To alleviate this issue, this work proposes an energy-based model for WLAC, which enables the context hidden vector to capture crucial information from the source sentence. Unfortunately, training and inference suffer from efficiency and effectiveness challenges, therefore we employ three simple yet effective strategies to put our model into practice. Experiments on four standard benchmarks demonstrate that our reranking-based approach achieves substantial improvements (about 6.07%) over the previous state-of-the-art model. Further analyses show that each strategy of our approach contributes to the final performance.1","PeriodicalId":506323,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":"24 1","pages":"137-156"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139891085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation 基于能量的计算机辅助翻译单词级自动补全模型
Transactions of the Association for Computational Linguistics Pub Date : 2024-02-01 DOI: 10.1162/tacl_a_00637
Cheng Yang, Guoping Huang, Mo Yu, Zhirui Zhang, Siheng Li, Mingming Yang, Shuming Shi, Yujiu Yang, Lemao Liu
{"title":"An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation","authors":"Cheng Yang, Guoping Huang, Mo Yu, Zhirui Zhang, Siheng Li, Mingming Yang, Shuming Shi, Yujiu Yang, Lemao Liu","doi":"10.1162/tacl_a_00637","DOIUrl":"https://doi.org/10.1162/tacl_a_00637","url":null,"abstract":"Abstract Word-level AutoCompletion (WLAC) is a rewarding yet challenging task in Computer-aided Translation. Existing work addresses this task through a classification model based on a neural network that maps the hidden vector of the input context into its corresponding label (i.e., the candidate target word is treated as a label). Since the context hidden vector itself does not take the label into account and it is projected to the label through a linear classifier, the model cannot sufficiently leverage valuable information from the source sentence as verified in our experiments, which eventually hinders its overall performance. To alleviate this issue, this work proposes an energy-based model for WLAC, which enables the context hidden vector to capture crucial information from the source sentence. Unfortunately, training and inference suffer from efficiency and effectiveness challenges, therefore we employ three simple yet effective strategies to put our model into practice. Experiments on four standard benchmarks demonstrate that our reranking-based approach achieves substantial improvements (about 6.07%) over the previous state-of-the-art model. Further analyses show that each strategy of our approach contributes to the final performance.1","PeriodicalId":506323,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":"1447 ","pages":"137-156"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139831090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Addressing the Binning Problem in Calibration Assessment through Scalar Annotations 通过标量注释解决校准评估中的分选问题
Transactions of the Association for Computational Linguistics Pub Date : 2024-02-01 DOI: 10.1162/tacl_a_00636
Zhengping Jiang, Anqi Liu, Benjamnin Van Durme
{"title":"Addressing the Binning Problem in Calibration Assessment through Scalar Annotations","authors":"Zhengping Jiang, Anqi Liu, Benjamnin Van Durme","doi":"10.1162/tacl_a_00636","DOIUrl":"https://doi.org/10.1162/tacl_a_00636","url":null,"abstract":"Abstract Computational linguistics models commonly target the prediction of discrete—categorical—labels. When assessing how well-calibrated these model predictions are, popular evaluation schemes require practitioners to manually determine a binning scheme: grouping labels into bins to approximate true label posterior. The problem is that these metrics are sensitive to binning decisions. We consider two solutions to the binning problem that apply at the stage of data annotation: collecting either distributed (redundant) labels or direct scalar value assignment. In this paper, we show that although both approaches address the binning problem by evaluating instance-level calibration, direct scalar assignment is significantly more cost-effective. We provide theoretical analysis and empirical evidence to support our proposal for dataset creators to adopt scalar annotation protocols to enable a higher-quality assessment of model calibration.","PeriodicalId":506323,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":"46 8","pages":"120-136"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139823555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Addressing the Binning Problem in Calibration Assessment through Scalar Annotations 通过标量注释解决校准评估中的分选问题
Transactions of the Association for Computational Linguistics Pub Date : 2024-02-01 DOI: 10.1162/tacl_a_00636
Zhengping Jiang, Anqi Liu, Benjamnin Van Durme
{"title":"Addressing the Binning Problem in Calibration Assessment through Scalar Annotations","authors":"Zhengping Jiang, Anqi Liu, Benjamnin Van Durme","doi":"10.1162/tacl_a_00636","DOIUrl":"https://doi.org/10.1162/tacl_a_00636","url":null,"abstract":"Abstract Computational linguistics models commonly target the prediction of discrete—categorical—labels. When assessing how well-calibrated these model predictions are, popular evaluation schemes require practitioners to manually determine a binning scheme: grouping labels into bins to approximate true label posterior. The problem is that these metrics are sensitive to binning decisions. We consider two solutions to the binning problem that apply at the stage of data annotation: collecting either distributed (redundant) labels or direct scalar value assignment. In this paper, we show that although both approaches address the binning problem by evaluating instance-level calibration, direct scalar assignment is significantly more cost-effective. We provide theoretical analysis and empirical evidence to support our proposal for dataset creators to adopt scalar annotation protocols to enable a higher-quality assessment of model calibration.","PeriodicalId":506323,"journal":{"name":"Transactions of the Association for Computational Linguistics","volume":"8 1","pages":"120-136"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139883328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信