Multi-Round Extraction and Dynamic Role Selection Framework For Document-Level Event Extraction
Pages 130 - 134
Abstract
Document-level Event Extraction (DEE) aims to extract structured event information from a document, which is an indispensable downstream task for many NLP applications. Argument-scatter and multi-event are its two main challenges. Recent work decomposes this challenging DEE task into multiple steps such as entity recognition, contextual information modeling, and event arguments extraction. Besides, they all extract event arguments in a predefined fixed role order. Though it is effective, its cumbersome steps and fixed extraction order will bring about the problem of error propagation. To address this issue, we propose a Multi-round Extraction and Dynamic Role Selection (MREDRS) Framework for the DEE task. We model the DEE task in an end-to-end manner to avoid these cumbersome steps. Multi-round extraction can deal with multi-event problem. In order to avoid the error propagation problem caused by the fixed role extraction order, we dynamically select the next role according to the current extraction state. We conducted experiments on the commonly used DEE dataset and extensive experimental results demonstrated the effectiveness of our method.
References
[1]
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473(2014).
[2]
Yubo Chen, Liheng Xu, Kang Liu, Daojian Zeng, and Jun Zhao. 2015. Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL 2015, July 26-31, 2015, Beijing, China, Volume 1: Long Papers. The Association for Computer Linguistics, 167–176. https://doi.org/10.3115/v1/p15-1017
[3]
Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, Alessandro Moschitti, Bo Pang, and Walter Daelemans (Eds.). ACL, 1724–1734. https://doi.org/10.3115/v1/d14-1179
[4]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, 4171–4186. https://doi.org/10.18653/v1/n19-1423
[5]
Xinya Du and Claire Cardie. 2020. Event Extraction by Answering (Almost) Natural Questions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, 671–683. https://doi.org/10.18653/v1/2020.emnlp-main.49
[6]
Xinya Du, Alexander M. Rush, and Claire Cardie. 2021. GRIT: Generative Role-filler Transformers for Document-level Event Entity Extraction. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, EACL 2021, Online, April 19 - 23, 2021, Paola Merlo, Jörg Tiedemann, and Reut Tsarfaty (Eds.). Association for Computational Linguistics, 634–644. https://doi.org/10.18653/v1/2021.eacl-main.52
[7]
Yu Hong, Jianfeng Zhang, Bin Ma, Jianmin Yao, Guodong Zhou, and Qiaoming Zhu. 2011. Using cross-entity inference to improve event extraction. In Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies. 1127–1136.
[8]
Lifu Huang, Taylor Cassidy, Xiaocheng Feng, Heng Ji, Clare R. Voss, Jiawei Han, and Avirup Sil. 2016. Liberal Event Extraction and Event Schema Induction. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers. The Association for Computer Linguistics. https://doi.org/10.18653/v1/p16-1025
[9]
Lifu Huang, Heng Ji, Kyunghyun Cho, Ido Dagan, Sebastian Riedel, and Clare R. Voss. 2018. Zero-Shot Transfer Learning for Event Extraction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers, Iryna Gurevych and Yusuke Miyao (Eds.). Association for Computational Linguistics, 2160–2170. https://doi.org/10.18653/v1/P18-1201
[10]
Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=SJU4ayYgl
[11]
Qi Li, Heng Ji, and Liang Huang. 2013. Joint Event Extraction via Structured Prediction with Global Features. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013, 4-9 August 2013, Sofia, Bulgaria, Volume 1: Long Papers. The Association for Computer Linguistics, 73–82. https://aclanthology.org/P13-1008/
[12]
Sha Li, Heng Ji, and Jiawei Han. 2021. Document-Level Event Argument Extraction by Conditional Generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tür, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou (Eds.). Association for Computational Linguistics, 894–908. https://doi.org/10.18653/v1/2021.naacl-main.69
[13]
Xiao Liu, Heyan Huang, and Yue Zhang. 2019. Open Domain Event Extraction Using Neural Latent Variable Models. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, Anna Korhonen, David R. Traum, and Lluís Màrquez (Eds.). Association for Computational Linguistics, 2860–2871. https://doi.org/10.18653/v1/p19-1276
[14]
Xiao Liu, Zhunchen Luo, and Heyan Huang. 2018. Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun’ichi Tsujii (Eds.). Association for Computational Linguistics, 1247–1256. https://doi.org/10.18653/v1/d18-1156
[15]
Yaojie Lu, Hongyu Lin, Jin Xu, Xianpei Han, Jialong Tang, Annan Li, Le Sun, Meng Liao, and Shaoyi Chen. 2021. Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.). Association for Computational Linguistics, 2795–2806. https://doi.org/10.18653/v1/2021.acl-long.217
[16]
Thien Huu Nguyen, Kyunghyun Cho, and Ralph Grishman. 2016. Joint Event Extraction via Recurrent Neural Networks. In NAACL HLT 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego California, USA, June 12-17, 2016, Kevin Knight, Ani Nenkova, and Owen Rambow (Eds.). The Association for Computational Linguistics, 300–309. https://doi.org/10.18653/v1/n16-1034
[17]
Lei Sha, Feng Qian, Baobao Chang, and Zhifang Sui. 2018. Jointly Extracting Event Triggers and Arguments by Dependency-Bridge RNN and Tensor-Based Argument Interaction. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press, 5916–5923. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16222
[18]
Beth Sundheim. 1992. Overview of the fourth message understanding evaluation and conference. In Proceedings of the 4th Conference on Message Understanding, MUC 1992, McLean, Virginia, USA, June 16-18, 1992. ACL, 3–21. https://doi.org/10.3115/1072064.1072066
[19]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 5998–6008. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
[20]
Ziqi Wang, Xiaozhi Wang, Xu Han, Yankai Lin, Lei Hou, Zhiyuan Liu, Peng Li, Juanzi Li, and Jie Zhou. 2021. CLEVE: Contrastive Pre-training for Event Extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.). Association for Computational Linguistics, 6283–6297. https://doi.org/10.18653/v1/2021.acl-long.491
[21]
Runxin Xu, Tianyu Liu, Lei Li, and Baobao Chang. 2021. Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.). Association for Computational Linguistics, 3533–3546. https://doi.org/10.18653/v1/2021.acl-long.274
[22]
Hang Yang, Yubo Chen, Kang Liu, Yang Xiao, and Jun Zhao. 2018. Dcfee: A document-level chinese financial event extraction system based on automatically labeled training data. In Proceedings of ACL 2018, System Demonstrations. 50–55.
[23]
Hang Yang, Dianbo Sui, Yubo Chen, Kang Liu, Jun Zhao, and Taifeng Wang. 2021. Document-level event extraction via parallel prediction networks. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 6298–6308.
[24]
Sen Yang, Dawei Feng, Linbo Qiao, Zhigang Kan, and Dongsheng Li. 2019. Exploring Pre-trained Language Models for Event Extraction and Generation. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, Anna Korhonen, David R. Traum, and Lluís Màrquez (Eds.). Association for Computational Linguistics, 5284–5294. https://doi.org/10.18653/v1/p19-1522
[25]
Zhisong Zhang, Xiang Kong, Zhengzhong Liu, Xuezhe Ma, and Eduard H. Hovy. 2020. A Two-Step Approach for Implicit Event Argument Detection. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault (Eds.). Association for Computational Linguistics, 7479–7485. https://doi.org/10.18653/v1/2020.acl-main.667
[26]
Liang Zhao. 2021. Event Prediction in the Big Data Era: A Systematic Survey. ACM Comput. Surv. 54, 5 (2021), 94:1–94:37. https://doi.org/10.1145/3450287
[27]
Shun Zheng, Wei Cao, Wei Xu, and Jiang Bian. 2019. Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, 337–346. https://doi.org/10.18653/v1/D19-1032
Index Terms
- Multi-Round Extraction and Dynamic Role Selection Framework For Document-Level Event Extraction
Recommendations
Multi-Turn and Multi-Granularity Reader for Document-Level Event Extraction
Most existing event extraction works mainly focus on extracting events from one sentence. However, in real-world applications, arguments of one event may scatter across sentences and multiple events may co-occur in one document. Thus, these scenarios ...
A Unified Information Extraction System Based on Role Recognition and Combination
Natural Language Processing and Chinese ComputingAbstractIn this paper, we propose a unified information extraction system, which handles event extraction (EE) and relation extraction (RE) tasks. Given context and schema, event extraction aims to extract the events and the specific roles in the events, ...
Explainable document-level event extraction via back-tracing to sentence-level event clues
AbstractDocument-level event extraction (DEE) aims to extract events directly from a document. Current state-of-the-art methods follow the entity-centric framework, capturing sentence-level event clues into entity instance representations for ...
Graphical abstractDisplay Omitted
Highlights- An explainable document-level event extraction framework ExpDEE is proposed.
- ...
Comments
Information & Contributors
Information
Published In
December 2022
406 pages
ISBN:9781450399067
DOI:10.1145/3578741
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 06 March 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
MLNLP 2022
MLNLP 2022: 2022 5th International Conference on Machine Learning and Natural Language Processing
December 23 - 25, 2022
Sanya, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 41Total Downloads
- Downloads (Last 12 months)5
- Downloads (Last 6 weeks)0
Reflects downloads up to 01 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format