An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution

Ryuto Konno, Yuichiroh Matsubayashi, Shun Kiyono, Hiroki Ouchi, Ryo Takahashi, Kentaro Inui


Abstract
One critical issue of zero anaphora resolution (ZAR) is the scarcity of labeled data. This study explores how effectively this problem can be alleviated by data augmentation. We adopt a state-of-the-art data augmentation method, called the contextual data augmentation (CDA), that generates labeled training instances using a pretrained language model. The CDA has been reported to work well for several other natural language processing tasks, including text classification and machine translation. This study addresses two underexplored issues on CDA, that is, how to reduce the computational cost of data augmentation and how to ensure the quality of the generated data. We also propose two methods to adapt CDA to ZAR: [MASK]-based augmentation and linguistically-controlled masking. Consequently, the experimental results on Japanese ZAR show that our methods contribute to both the accuracy gainand the computation cost reduction. Our closer analysis reveals that the proposed method can improve the quality of the augmented training data when compared to the conventional CDA.
Anthology ID:
2020.coling-main.435
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
4956–4968
Language:
URL:
https://aclanthology.org/2020.coling-main.435
DOI:
10.18653/v1/2020.coling-main.435
Bibkey:
Cite (ACL):
Ryuto Konno, Yuichiroh Matsubayashi, Shun Kiyono, Hiroki Ouchi, Ryo Takahashi, and Kentaro Inui. 2020. An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4956–4968, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution (Konno et al., COLING 2020)
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PDF:
https://aclanthology.org/2020.coling-main.435.pdf