Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction

Jian Liu, Yufeng Chen, Jinan Xu


Abstract
Implicit event argument extraction (EAE) is a crucial document-level information extraction task that aims to identify event arguments beyond the sentence level. Despite many efforts for this task, the lack of enough training data has long impeded the study. In this paper, we take a new perspective to address the data sparsity issue faced by implicit EAE, by bridging the task with machine reading comprehension (MRC). Particularly, we devise two data augmentation regimes via MRC, including: 1) implicit knowledge transfer, which enables knowledge transfer from other tasks, by building a unified training framework in the MRC formulation, and 2) explicit data augmentation, which can explicitly generate new training examples, by treating MRC models as an annotator. The extensive experiments have justified the effectiveness of our approach — it not only obtains state-of-the-art performance on two benchmarks, but also demonstrates superior results in a data-low scenario.
Anthology ID:
2021.emnlp-main.214
Original:
2021.emnlp-main.214v1
Version 2:
2021.emnlp-main.214v2
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2716–2725
Language:
URL:
https://aclanthology.org/2021.emnlp-main.214
DOI:
10.18653/v1/2021.emnlp-main.214
Bibkey:
Cite (ACL):
Jian Liu, Yufeng Chen, and Jinan Xu. 2021. Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2716–2725, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction (Liu et al., EMNLP 2021)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.emnlp-main.214.pdf
Video:
 https://preview.aclanthology.org/ingestion-script-update/2021.emnlp-main.214.mp4
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