ABSTRACT
Retrieving event instances from texts is pivotal to various natural language processing applications (e.g., automatic question answering and dialogue systems), and the first task to perform is event detection. There are two related sub-tasks therein-trigger identification and type classification, and the former is considered to play a dominant role. Nevertheless, it is notoriously challenging to predict event triggers right. To handle the task, existing work has made tremendous progress by incorporating manual features, data augmentation and neural networks, etc. Due to the scarcity of data and insufficient representation of trigger words, however, they still fail to precisely determine the spans of triggers (coined as trigger span detection problem). To address the challenge, we propose to learn discriminative neural representations (DNR) from texts. Specifically, our DNR model tackles the trigger span detection problem by exploiting two novel techniques: 1) a contrastive learning strategy, which enlarges the discrepancy between representations of words inside and outside triggers; and 2) a Mixspan strategy, which better trains the model to differentiate words nearby triggers' span boundaries. Extensive experiments on benchmarks-ACE2005 and TAC2015-demonstrate the superiority of our DNR model, leading to state-of-the-art performance.
- Pierpaolo Basile, Annalina Caputo, Giovanni Semeraro, and Lucia Siciliani. 2014. Extending an Information Retrieval System through Time Event Extraction. In Proceedings of the 8th International Workshop on Information Filtering and Retrieval colocated with XIII AI*IA Symposium on Artificial Intelligence (AI*IA 2014), Pisa,Italy, December 10, 2014. 36--47.Google Scholar
- Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi-relational Data. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5--8, 2013, Lake Tahoe, Nevada, United States. 2787--2795.Google ScholarDigital Library
- Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020,13--18 July 2020, Virtual Event (Proceedings of Machine Learning Research, Vol. 119). PMLR, 1597--1607.Google Scholar
- 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. 167--176.Google ScholarCross Ref
- Pengxiang Cheng and Katrin Erk. 2018. Implicit Argument Prediction with Event Knowledge. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1--6, 2018, Volume 1 (Long Papers). 831--840.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, NAACL-HLT 2019, Minneapolis, MN, USA, June 2--7, 2019, Volume 1 (Long and Short Papers). 4171--4186.Google Scholar
- Alexey Dosovitskiy, Jost Tobias Springenberg, Martin A. Riedmiller, and Thomas Brox. 2014. Discriminative Unsupervised Feature Learning with Convolutional Neural Networks. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8--13 2014,Montreal, Quebec, Canada. 766--774.Google Scholar
- Joe Ellis, Jeremy Getman, Dana Fore, Neil Kuster, Zhiyi Song, Ann Bies, and Stephanie M. Strassel. 2015. Overview of Linguistic Resources for the TAC KBP2015 Evaluations: Methodologies and Results. In Proceedings of the 2015 Text Analysis Conference, TAC 2015, Gaithersburg, Maryland, USA, November 16--17, 2015, 2015.Google Scholar
- Christiane Fellbaum. 2005. WordNet and wordnets. In Encyclopedia of Language and Linguistics, Second Edition, Oxford: Elsevier. 665--670.Google Scholar
- Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep Sparse Rectifier Neural Networks. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2011, Fort Lauderdale, USA, April11--13, 2011. 315--323.Google Scholar
- Raia Hadsell, Sumit Chopra, and Yann LeCun. 2006. Dimensionality Reduction by Learning an Invariant Mapping. In2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), 17--22 June 2006, New York, NY, USA. 1735--1742.Google ScholarDigital Library
- Peixin Huang, Xiang Zhao, Ryuichi Takanobu, Zhen Tan, and Weidong Xiao. 2020. Joint Event Extraction with Hierarchical Policy Network. In Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8--13, 2020. 2653--2664.Google ScholarCross Ref
- Heng Ji and Ralph Grishman. 2008. Refining Event Extraction through Cross-Document Inference. In ACL 2008, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, June 15--20, 2008, Columbus, Ohio, USA.254--262.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, ACL 2013, 4--9 August 2013, Sofia, Bulgaria, 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 ACL 2010, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, July 11--16, 2010, Uppsala, Sweden. 789--797.Google Scholar
- Jian Liu, Yubo Chen, Kang Liu, and Jun Zhao. 2018. Event Detection via Gated Multilingual Attention Mechanism. 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. 4865--4872.Google ScholarCross Ref
- Shulin Liu, Kang Liu, Shizhu He, and Jun Zhao. 2016. A Probabilistic Soft Logic Based Approach to Exploiting Latent and Global Information in Event Classification. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12--17, 2016, Phoenix, Arizona, USA. 2993--2999.Google ScholarDigital Library
- 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. 1247--1256.Google ScholarCross Ref
- Yaojie Lu, Hongyu Lin, Xianpei Han, and Le Sun. 2019. Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning. 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. 4366--4376.Google ScholarCross Ref
- 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. 300--309.Google ScholarCross Ref
- Thien Huu Nguyen and Ralph Grishman. 2018. Graph Convolutional Networks With Argument-Aware Pooling for Event Detection. 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. 5900--5907.Google ScholarCross Ref
- Sebastian Riedel and Andrew McCallum. [n.d.]. Fast and Robust Joint Models for Biomedical Event Extraction. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, 27--31 July 2011, John McIntyre Conference Centre, Edinburgh, UK, A meeting of SIGDAT, a Special Interest Group of the ACL. 1--12.Google Scholar
- Meihan Tong, Bin Xu, Shuai Wang, Yixin Cao, Lei Hou, Juanzi Li, and Jun Xie. 2020. Improving Event Detection via Open-domain Trigger Knowledge. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5--10, 2020. 5887--5897.Google ScholarCross Ref
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is Allyou Need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4--9 December 2017, Long Beach, CA, USA. 5998--6008.Google Scholar
- Xiaozhi Wang, Xu Han, Zhiyuan Liu, Maosong Sun, and Peng Li. 2019. Adversarial Training for Weakly Supervised Event Detection. 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). 998--1008.Google ScholarCross Ref
- Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, JuanziLi, Peng Li, Yankai Lin, and Jie Zhou. 2020. MAVEN: A Massive General Domain Event Detection Dataset. 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, 1652--1671.Google ScholarCross Ref
- Xiaozhi Wang, Ziqi Wang, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, MaosongSun, 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 2019, Hong Kong, China, November 3--7, 2019. 5776--5782.Google ScholarCross Ref
- Jason W. Wei and Kai Zou. 2019. EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks. 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 2019, Hong Kong, China, November 3--7, 2019. 6381--6387.Google Scholar
- Hui Yang, Tat-Seng Chua, Shuguang Wang, and Chun-Keat Koh. 2003. Structured use of external knowledge for event-based open domain question answering. In SIGIR 2003: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 28 - August 1, 2003, Toronto, Canada. 33--40.Google ScholarDigital Library
- 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.5284--5294.Google ScholarCross Ref
- Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz. 2018. mixup: Beyond Empirical Risk Minimization. In6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings.Google Scholar
- Fengbin Zhu, Wenqiang Lei, Chao Wang, Jianming Zheng, Soujanya Poria, and Tat-Seng Chua. 2021. Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering. CoRRabs/2101.00774 (2021).Google Scholar
Index Terms
- Learning Discriminative Neural Representations for Event Detection
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