Boosting Event Extraction with Denoised Structure-to-Text Augmentation

Bo Wang, Heyan Huang, Xiaochi Wei, Ge Shi, Xiao Liu, Chong Feng, Tong Zhou, Shuaiqiang Wang, Dawei Yin

Abstract
Event extraction aims to recognize pre-defined event triggers and arguments from texts, which suffer from the lack of high-quality annotations. In most NLP applications, involving a large scale of synthetic training data is a practical and effective approach to alleviate the problem of data scarcity. However, when applying to the task of event extraction, recent data augmentation methods often neglect the problem of grammatical incorrectness, structure misalignment, and semantic drifting, leading to unsatisfactory performances. In order to solve these problems, we propose a denoised structure-to-text augmentation framework for event extraction (DAEE), which generates additional training data through the knowledge-based structure-to-text generation model and selects the effective subset from the generated data iteratively with a deep reinforcement learning agent. Experimental results on several datasets demonstrate that the proposed method generates more diverse text representations for event extraction and achieves comparable results with the state-of-the-art.
Anthology ID:
2023.findings-acl.716
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11267–11281
Language:
URL:
https://aclanthology.org/2023.findings-acl.716
DOI:
10.18653/v1/2023.findings-acl.716
Bibkey:
Cite (ACL):
Bo Wang, Heyan Huang, Xiaochi Wei, Ge Shi, Xiao Liu, Chong Feng, Tong Zhou, Shuaiqiang Wang, and Dawei Yin. 2023. Boosting Event Extraction with Denoised Structure-to-Text Augmentation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 11267–11281, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Boosting Event Extraction with Denoised Structure-to-Text Augmentation (Wang et al., Findings 2023)
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