skip to main content
research-article

Multi-Turn and Multi-Granularity Reader for Document-Level Event Extraction

Published: 27 December 2022 Publication History

Abstract

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 require document-level event extraction (DEE), which aims to extract events and their arguments across sentences from a document. Previous works cast DEE as a two-step paradigm: sentence-level event extraction (SEE) to document-level event fusion. However, this paradigm lacks integrating document-level information for SEE and suffers from the inherent limitations of error propagation. In this article, we propose a multi-turn and multi-granularity reader for DEE that can extract events from the document directly without the stage of preliminary SEE. Specifically, we propose a new paradigm of DEE by formulating it as a machine reading comprehension task (i.e., the extraction of event arguments is transformed to identify the answer span from the document). Beyond the framework of machine reading comprehension, we introduce a multi-turn and multi-granularity reader to capture the dependencies between arguments explicitly and model long texts effectively. The empirical results demonstrate that our method achieves superior performance on the MUC-4 and the ChFinAnn datasets.

References

[1]
Jonathan Berant, Vivek Srikumar, Pei-Chun Chen, Abby Vander Linden, Brittany Harding, Brad Huang, Peter Clark, and Christopher D. Manning. 2014. Modeling biological processes for reading comprehension. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1499–1510.
[2]
Jari Björne and Tapio Salakoski. 2018. Biomedical event extraction using convolutional neural networks and dependency parsing. In Proceedings of the BioNLP 2018 Workshop. 98–108.
[3]
Yee Seng Chan, Joshua Fasching, Haoling Qiu, and Bonan Min. 2019. Rapid customization for event extraction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. 31–36.
[4]
Pei Chen, Hang Yang, Kang Liu, Ruihong Huang, Yubo Chen, Taifeng Wang, and Jun Zhao. 2020. Reconstructing event regions for event extraction via graph attention networks. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing. 811–820. https://www.aclweb.org/anthology/2020.aacl-main.81.
[5]
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 (Volume 1: Long Papers). 167–176.
[6]
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 (Volume 1: Long and Short Papers). 4171–4186.
[7]
George R. Doddington, Alexis Mitchell, Mark A. Przybocki, Lance A. Ramshaw, Stephanie M. Strassel, and Ralph M. Weischedel. 2004. The automatic content extraction (ACE) program-tasks, data, and evaluation. In Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC’04).
[8]
Xinya Du and Claire Cardie. 2020. Document-level event role filler extraction using multi-granularity contextualized encoding. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 8010–8020.
[9]
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’20). 671–683.
[10]
Xinya Du, Alexander Rush, and Claire Cardie. 2020. Document-level event-based extraction using generative template-filling transformers. arXiv preprint arXiv:2008.09249 (2020).
[11]
Ruihong Huang and Ellen Riloff. 2011. Peeling back the layers: Detecting event role fillers in secondary contexts. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (Volume 1). 1137–1147.
[12]
Ruihong Huang and Ellen Riloff. 2012. Bootstrapped training of event extraction classifiers. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics. 286–295.
[13]
Heng Ji and Ralph Grishman. 2011. Knowledge base population: Successful approaches and challenges. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 1148–1158.
[14]
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017. TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1601–1611.
[15]
Diederik P. Kingma and Jimmy Ba. 2014. ADAM: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
[16]
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017. RACE: Large-scale ReAding comprehension dataset from examinations. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 785–794.
[17]
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017. Zero-shot relation extraction via reading comprehension. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL’17). 333–342.
[18]
Fayuan Li, Weihua Peng, Yuguang Chen, Quan Wang, Lu Pan, Yajuan Lyu, and Yong Zhu. 2020. Event extraction as multi-turn question answering. In Findings of the Association for Computational Linguistics: EMNLP 2020. 829–838.
[19]
Manling Li, Qi Zeng, Ying Lin, Kyunghyun Cho, Heng Ji, Jonathan May, Nathanael Chambers, and Clare Voss. 2020. Connecting the dots: Event graph schema induction with path language modeling. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP’20). 684–695.
[20]
Xiaoya Li, Jingrong Feng, Yuxian Meng, Qinghong Han, Fei Wu, and Jiwei Li. 2020. A unified MRC framework for named entity recognition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 5849–5859.
[21]
Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, and Jiwei Li. 2019. Entity-relation extraction as multi-turn question answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 1340–1350.
[22]
Jian Liu, Yubo Chen, Kang Liu, Wei Bi, and Xiaojiang Liu. 2020. Event extraction as machine reading comprehension. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP’20). 1641–1651.
[23]
Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. 2018. The natural language decathlon: Multitask learning as question answering. arXiv preprint arXiv:1806.08730 (2018).
[24]
MUC-4. 1992. Fourth Message Understanding Conference (MUC-4): Proceedings of a Conference Held in McLean, Virginia, June 16-18, 1992. Morgan Kaufman, San Mateo, CA. https://www.aclweb.org/anthology/M92-1000.
[25]
Thien Huu Nguyen, Kyunghyun Cho, and Ralph Grishman. 2016. Joint event extraction via recurrent neural networks. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 300–309.
[26]
Siddharth Patwardhan and Ellen Riloff. 2009. A unified model of phrasal and sentential evidence for information extraction. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (Volume 1). 151–160.
[27]
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2383–2392.
[28]
Alan Ramponi, Rob van der Goot, Rosario Lombardo, and Barbara Plank. 2020. Biomedical event extraction as sequence labeling. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP’20). 5357–5367.
[29]
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016. Bidirectional attention flow for machine comprehension. arXiv preprint arXiv:1611.01603 (2016).
[30]
Yelong Shen, Po-Sen Huang, Jianfeng Gao, and Weizhu Chen. 2017. ReasoNet: Learning to stop reading in machine comprehension. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1047–1055.
[31]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998–6008.
[32]
Shuohang Wang and Jing Jiang. 2016. Machine comprehension using match-LSTM and answer pointer. arXiv preprint arXiv:1608.07905 (2016).
[33]
Xiaozhi Wang, Ziqi Wang, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Maosong Sun, Jie Zhou, and Xiang Ren. 2019. HMEAE: Hierarchical modular event argument 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’19). 5781–5787.
[34]
Zhiguo Wang, Patrick Ng, Xiaofei Ma, Ramesh Nallapati, and Bing Xiang. 2019. Multi-passage BERT: A globally normalized BERT model for open-domain question answering. arXiv preprint arXiv:1908.08167 (2019).
[35]
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 (Volume 1: Long Papers). 3533–3546.
[36]
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.
[37]
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 Annual Meeting of the Association for Computational Linguistics. 5284–5294.
[38]
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2369–2380.
[39]
Bo Zheng, Haoyang Wen, Yaobo Liang, Nan Duan, Wanxiang Che, Daxin Jiang, Ming Zhou, and Ting Liu. 2020. Document modeling with graph attention networks for multi-grained machine reading comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 6708–6718.
[40]
Shun Zheng, Wei Cao, Wei Xu, and Jiang Bian. 2019. Doc2EDAG: An end-to-end document-level framework for Chinese financial event extraction. arXiv preprint arXiv:1904.07535 (2019).

Cited By

View all
  • (2024)Real-Time Extraction of News Events Based on BERT ModelInternational Journal of Advanced Network, Monitoring and Controls10.2478/ijanmc-2024-00239:3(24-31)Online publication date: 30-Sep-2024
  • (2024)SIAT: Document-level Event Extraction via Spatiality-Augmented Interaction Model with Adaptive ThresholdingACM Transactions on Asian and Low-Resource Language Information Processing10.1145/3698261Online publication date: 7-Oct-2024
  • (2024)CDMG:Combing DeBERTa-v2 Model with Globalpointer for Chinese Text Level Event Extraction2024 China Automation Congress (CAC)10.1109/CAC63892.2024.10865381(3890-3895)Online publication date: 1-Nov-2024
  • Show More Cited By

Index Terms

  1. Multi-Turn and Multi-Granularity Reader for Document-Level Event Extraction

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Transactions on Asian and Low-Resource Language Information Processing
    ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 22, Issue 2
    February 2023
    624 pages
    ISSN:2375-4699
    EISSN:2375-4702
    DOI:10.1145/3572719
    Issue’s Table of Contents

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 27 December 2022
    Online AM: 11 June 2022
    Accepted: 24 May 2022
    Revised: 22 February 2022
    Received: 08 October 2021
    Published in TALLIP Volume 22, Issue 2

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Document-level event extraction
    2. machine reading comprehension
    3. multi-granularity reader

    Qualifiers

    • Research-article
    • Refereed

    Funding Sources

    • National Key Research and Development Program of China
    • National Natural Science Foundation of China
    • Strategic Priority Research Program of the Chinese Academy of Sciences
    • CCF-Tencent Open Research Fund, the Youth Innovation Promotion Association CAS
    • Yunnan Provincial Major Science and Technology Special Plan Projects

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)102
    • Downloads (Last 6 weeks)5
    Reflects downloads up to 20 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Real-Time Extraction of News Events Based on BERT ModelInternational Journal of Advanced Network, Monitoring and Controls10.2478/ijanmc-2024-00239:3(24-31)Online publication date: 30-Sep-2024
    • (2024)SIAT: Document-level Event Extraction via Spatiality-Augmented Interaction Model with Adaptive ThresholdingACM Transactions on Asian and Low-Resource Language Information Processing10.1145/3698261Online publication date: 7-Oct-2024
    • (2024)CDMG:Combing DeBERTa-v2 Model with Globalpointer for Chinese Text Level Event Extraction2024 China Automation Congress (CAC)10.1109/CAC63892.2024.10865381(3890-3895)Online publication date: 1-Nov-2024
    • (2023)DEEDP: Document-Level Event Extraction Model Incorporating Dependency PathsApplied Sciences10.3390/app1305284613:5(2846)Online publication date: 22-Feb-2023

    View Options

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Full Text

    View this article in Full Text.

    Full Text

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media