@inproceedings{wang-etal-2023-boosting,
title = "Boosting Event Extraction with Denoised Structure-to-Text Augmentation",
author = "Wang, Bo and
Huang, Heyan and
Wei, Xiaochi and
Shi, Ge and
Liu, Xiao and
Feng, Chong and
Zhou, Tong and
Wang, Shuaiqiang and
Yin, Dawei",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.716",
doi = "10.18653/v1/2023.findings-acl.716",
pages = "11267--11281",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Boosting Event Extraction with Denoised Structure-to-Text Augmentation
%A Wang, Bo
%A Huang, Heyan
%A Wei, Xiaochi
%A Shi, Ge
%A Liu, Xiao
%A Feng, Chong
%A Zhou, Tong
%A Wang, Shuaiqiang
%A Yin, Dawei
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wang-etal-2023-boosting
%X 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.
%R 10.18653/v1/2023.findings-acl.716
%U https://aclanthology.org/2023.findings-acl.716
%U https://doi.org/10.18653/v1/2023.findings-acl.716
%P 11267-11281
Markdown (Informal)
[Boosting Event Extraction with Denoised Structure-to-Text Augmentation](https://aclanthology.org/2023.findings-acl.716) (Wang et al., Findings 2023)
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.