@inproceedings{zhang-etal-2020-two,
title = "A Two-Step Approach for Implicit Event Argument Detection",
author = "Zhang, Zhisong and
Kong, Xiang and
Liu, Zhengzhong and
Ma, Xuezhe and
Hovy, Eduard",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.667",
doi = "10.18653/v1/2020.acl-main.667",
pages = "7479--7485",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Two-Step Approach for Implicit Event Argument Detection
%A Zhang, Zhisong
%A Kong, Xiang
%A Liu, Zhengzhong
%A Ma, Xuezhe
%A Hovy, Eduard
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-two
%X 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.
%R 10.18653/v1/2020.acl-main.667
%U https://aclanthology.org/2020.acl-main.667
%U https://doi.org/10.18653/v1/2020.acl-main.667
%P 7479-7485
Markdown (Informal)
[A Two-Step Approach for Implicit Event Argument Detection](https://aclanthology.org/2020.acl-main.667) (Zhang et al., ACL 2020)
ACL
- 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, pages 7479–7485, Online. Association for Computational Linguistics.