Hang Yang
ABSTRACT
Multi-sentence argument linking aims at detecting implicit event arguments across sentences, which is indispensable when textual events span across multiple sentences in a document. Previous studies suffer from the inherent limitations of error propagation and lack the explicit modeling of the local and non-local interactions in a textual event. In this paper, we propose an event-aware hierarchical encoder for multi-sentence argument linking. Specifically, we introduce a hierarchical encoder to explicitly capture the local and global interactions in a textual event. Furthermore, we introduce an auxiliary task to predict the event-relevant context in a manner of multi-task learning, which can implicitly benefit the argument linking model to be aware of the event-relevant context. The empirical results on the widely used argument linking dataset show that our model significantly outperforms the baselines, which demonstrates the effectiveness of our proposed method.
Supplemental Material
- David Ahn. 2006. The stages of event extraction. In Proceedings of the Workshop on Annotating and Reasoning about Time and Events. 1--8. Google Scholar
Digital Library
- 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 ACL.Google ScholarCross Ref
- 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). Association for Computational Linguistics, Minneapolis, Minnesota, 4171--4186. https://doi.org/10.18653/v1/N19--1423Google Scholar
- 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 Lrec, Vol. 2. Lisbon, 1.Google Scholar
- Seth Ebner, Patrick Xia, Ryan Culkin, Kyle Rawlins, and Benjamin Van Durme. 2020. Multi-Sentence Argument Linking. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 8057--8077. https://doi.org/10.18653/v1/2020.acl-main.718Google ScholarCross Ref
- 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. Google ScholarDigital Library
- Lifu Huang, Heng Ji, Kyunghyun Cho, Ido Dagan, Sebastian Riedel, and Clare Voss. 2018. Zero-Shot Transfer Learning for Event Extraction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Melbourne, Australia, 2160--2170. https://doi.org/10.18653/v1/P18--1201Google ScholarCross Ref
- Heng Ji and Ralph Grishman. 2008. Refining event extraction through cross-document inference. In Proceedings of ACL-08: Hlt. 254--262.Google Scholar
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google Scholar
- 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 (Volume 1: Long Papers). 73--82.Google Scholar
- Shasha Liao and Ralph Grishman. 2010. Using document level cross-event inference to improve event extraction. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. 789--797. Google ScholarDigital Library
- 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). Association for Computational Linguistics, Online, 1641--1651. https://doi.org/10.18653/v1/2020.emnlp-main.128Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Yeon Seonwoo, Ji-Hoon Kim, Jung-Woo Ha, and Alice Oh. 2020. Context-Aware Answer Extraction in Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 2418--2428. https://doi.org/10.18653/v1/2020.emnlp-main.189Google ScholarCross Ref
- Peng Shi and Jimmy Lin. 2019. Simple bert models for relation extraction and semantic role labeling. arXiv preprint arXiv:1904.05255 (2019).Google Scholar
- 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. Google ScholarDigital Library
- Bishan Yang and Tom M. Mitchell. 2016. Joint Extraction of Events and Entities within a Document Context. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, San Diego, California, 289--299. https://doi.org/10.18653/v1/N16--1033Google Scholar
- 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.Google ScholarCross Ref
- Zhisong Zhang, Xiang Kong, Zhengzhong Liu, Xuezhe Ma, and Eduard Hovy. 2020. A Two-Step Approach for Implicit Event Argument Detection. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 7479--7485.Google ScholarCross Ref
Index Terms
- Multi-Sentence Argument Linking via An Event-Aware Hierarchical Encoder
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