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ShanghaiTech University Knowledge Management System
| Neuralizing Regular Expressions for Slot Filling | |
| 2021 | |
| 会议录名称 | EMNLP 2021 - 2021 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, PROCEEDINGS |
| 页码 | 9481-9498 |
| 发表状态 | 已发表 |
| DOI | --- |
| 摘要 | Neural models and symbolic rules such as regular expressions have their respective merits and weaknesses. In this paper, we study the integration of the two approaches for the slot filling task by converting regular expressions into neural networks. Specifically, we first convert regular expressions into a special form of finite-state transducers, then unfold its approximate inference algorithm as a bidirectional recurrent neural model that performs slot filling via sequence labeling. Experimental results show that our model has superior zero-shot and few-shot performance and stays competitive when there are sufficient training data. © 2021 Association for Computational Linguistics |
| 会议录编者/会议主办者 | Amazon Science ; Apple ; Bloomberg Engineering ; et al. ; Facebook AI ; Google Research |
| 关键词 | Recurrent neural networks Inference engines Pattern matching Computational linguistics Approximate inference Finite state transducers Inference algorithm Neural modelling Neural-networks Performance Regular expressions Sequence Labeling Training data |
| 会议名称 | 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 |
| 会议地点 | Virtual, Punta Cana, Dominican republic |
| 会议日期 | November 7, 2021 - November 11, 2021 |
| URL | 查看原文 |
| 收录类别 | EI |
| 语种 | 英语 |
| 出版者 | Association for Computational Linguistics (ACL) |
| EI入藏号 | 20221411909992 |
| EI主题词 | Filling |
| EI分类号 | 691.2 Materials Handling Methods ; 721.1 Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory ; 723.4.1 Expert Systems |
| 原始文献类型 | Conference article (CA) |
| 文献类型 | 会议论文 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/251929 |
| 专题 | 信息科学与技术学院 信息科学与技术学院_PI研究组_屠可伟组 信息科学与技术学院_博士生 |
| 作者单位 | 1.School of Information Science and Technology, ShanghaiTech University, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai Engineering Research Center of Intelligent Vision and Imaging, China; 2.New York University, United States |
| 第一作者单位 | 信息科学与技术学院 |
| 第一作者的第一单位 | 信息科学与技术学院 |
| 推荐引用方式 GB/T 7714 | Jiang, Chengyue,Jin, Zijian,Tu, Kewei. Neuralizing Regular Expressions for Slot Filling[C]//Amazon Science, Apple, Bloomberg Engineering, et al., Facebook AI, Google Research:Association for Computational Linguistics (ACL),2021:9481-9498. |
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