A two-step approach for implicit event argument detection

Zhisong Zhang; Xiang Kong; Zhengzhong Liu; Xuezhe Ma; Eduard Hovy

doi:10.18653/v1/2020.acl-main.667

A two-step approach for implicit event argument detection

Zhisong Zhang, Xiang Kong, Zhengzhong Liu, Xuezhe Ma, Eduard Hovy

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

83 Citations (Scopus)

1 Downloads (CityUHK Scholars)

Abstract

In this work, we explore the implicit event argument detection task, which studies event arguments beyond sentence boundaries. The addition of cross-sentence argument candidates imposes great challenges for modeling. To reduce the number of candidates, we adopt a two-step approach, decomposing the problem into two sub-problems: argument head-word detection and head-to-span expansion. Evaluated on the recent RAMS dataset (Ebner et al., 2020), our model achieves overall better performance than a strong sequence labeling baseline. We further provide detailed error analysis, presenting where the model mainly makes errors and indicating directions for future improvements. It remains a challenge to detect implicit arguments, calling for more future work of document-level modeling for this task. © 2020 Association for Computational Linguistics

Original language	English
Title of host publication	ACL 2020 - The 58th Annual Meeting of the Association for Computational Linguistics
Subtitle of host publication	Proceedings of the Conference
Publisher	Association for Computational Linguistics
Pages	7479-7485
Number of pages	7
ISBN (Print)	9781952148255
DOIs	https://doi.org/10.18653/v1/2020.acl-main.667
Publication status	Published - Jul 2020
Externally published	Yes
Event	58th Annual Meeting of the Association for Computational Linguistics (ACL 2020) - Virtual, United States Duration: 5 Jul 2020 → 10 Jul 2020 https://acl2020.org/

Publication series

Name	Proceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)	0736-587X

Conference

Conference	58th Annual Meeting of the Association for Computational Linguistics (ACL 2020)
Abbreviated title	ACL2020
Place	United States
Period	5/07/20 → 10/07/20
Internet address	https://acl2020.org/

Funding

This research was supported in part by DARPA grant FA8750-18-2-0018 funded under the AIDA program. We thank the three anonymous reviewers for their helpful comments.

Publisher's Copyright Statement

This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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10.18653/v1/2020.acl-main.667

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Zhang, Z., Kong, X., Liu, Z., Ma, X., & Hovy, E. (2020). A two-step approach for implicit event argument detection. In ACL 2020 - The 58th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference (pp. 7479-7485). (Proceedings of the Annual Meeting of the Association for Computational Linguistics). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.667

Zhang, Zhisong ; Kong, Xiang ; Liu, Zhengzhong et al. / A two-step approach for implicit event argument detection. ACL 2020 - The 58th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference. Association for Computational Linguistics, 2020. pp. 7479-7485 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

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title = "A two-step approach for implicit event argument detection",

abstract = "In this work, we explore the implicit event argument detection task, which studies event arguments beyond sentence boundaries. The addition of cross-sentence argument candidates imposes great challenges for modeling. To reduce the number of candidates, we adopt a two-step approach, decomposing the problem into two sub-problems: argument head-word detection and head-to-span expansion. Evaluated on the recent RAMS dataset (Ebner et al., 2020), our model achieves overall better performance than a strong sequence labeling baseline. We further provide detailed error analysis, presenting where the model mainly makes errors and indicating directions for future improvements. It remains a challenge to detect implicit arguments, calling for more future work of document-level modeling for this task. {\textcopyright} 2020 Association for Computational Linguistics",

author = "Zhisong Zhang and Xiang Kong and Zhengzhong Liu and Xuezhe Ma and Eduard Hovy",

year = "2020",

month = jul,

doi = "10.18653/v1/2020.acl-main.667",

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Zhang, Z, Kong, X, Liu, Z, Ma, X & Hovy, E 2020, A two-step approach for implicit event argument detection. in ACL 2020 - The 58th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference. Proceedings of the Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, pp. 7479-7485, 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), United States, 5/07/20. https://doi.org/10.18653/v1/2020.acl-main.667

A two-step approach for implicit event argument detection. / Zhang, Zhisong; Kong, Xiang; Liu, Zhengzhong et al.
ACL 2020 - The 58th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference. Association for Computational Linguistics, 2020. p. 7479-7485 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AB - In this work, we explore the implicit event argument detection task, which studies event arguments beyond sentence boundaries. The addition of cross-sentence argument candidates imposes great challenges for modeling. To reduce the number of candidates, we adopt a two-step approach, decomposing the problem into two sub-problems: argument head-word detection and head-to-span expansion. Evaluated on the recent RAMS dataset (Ebner et al., 2020), our model achieves overall better performance than a strong sequence labeling baseline. We further provide detailed error analysis, presenting where the model mainly makes errors and indicating directions for future improvements. It remains a challenge to detect implicit arguments, calling for more future work of document-level modeling for this task. © 2020 Association for Computational Linguistics

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Zhang Z, Kong X, Liu Z, Ma X, Hovy E. A two-step approach for implicit event argument detection. In ACL 2020 - The 58th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference. Association for Computational Linguistics. 2020. p. 7479-7485. (Proceedings of the Annual Meeting of the Association for Computational Linguistics). doi: 10.18653/v1/2020.acl-main.667

A two-step approach for implicit event argument detection

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