AUGMENTATION STRATEGY OPTIMIZATION FOR LANGUAGE UNDERSTANDING

Chang Ting Chu*, Mahdin Rohmatillah, Ching Hsien Lee, Jen Tzung Chien

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

This paper presents a new language processing and understanding where an adaptive data augmentation strategy for individual documents is proposed instead of using one universal policy for the whole dataset. Importantly, a reinforcement learning and understanding method is exploited for document classification where the document encoder, augmenter and classifier are jointly optimized. In particular, a new reward function based on the consistency loss maximization is presented to assure the diversity of the generated documents. Using this method, the reward for adaptive augmentation policy is immediately calculated for every augmented instance without the need of waiting the child model performance metrics as the reward. The experiments on various classification tasks with a strong baseline model show that the augmentation strategy optimization can improve the model training process by providing meaningful augmentation data which eventually result in desirable evaluation performance. Furthermore, the extensive studies on the behavior of policy in different settings are provided in order to assure the diversity of the augmented data that was obtained by the proposed method.

原文English
主出版物標題2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面7952-7956
頁數5
ISBN(電子)9781665405409
DOIs
出版狀態Published - 2022
事件47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore
持續時間: 23 5月 202227 5月 2022

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2022-May
ISSN(列印)1520-6149

Conference

Conference47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
國家/地區Singapore
城市Virtual, Online
期間23/05/2227/05/22

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