Consistency Regularization for Cross-Lingual Fine-Tuning
Bo Zheng, Li Dong, Shaohan Huang, Wenhui Wang, Zewen Chi, Saksham Singhal, Wanxiang Che, Ting Liu
Abstract
Fine-tuning pre-trained cross-lingual language models can transfer task-specific supervision from one language to the others. In this work, we propose to improve cross-lingual fine-tuning with consistency regularization. Specifically, we use example consistency regularization to penalize the prediction sensitivity to four types of data augmentations, i.e., subword sampling, Gaussian noise, code-switch substitution, and machine translation. In addition, we employ model consistency to regularize the models trained with two augmented versions of the same training set. Experimental results on the XTREME benchmark show that our method significantly improves cross-lingual fine-tuning across various tasks, including text classification, question answering, and sequence labeling.
BibTeX
@inproceedings{zheng-etal-2021-consistency,
title = "Consistency Regularization for Cross-Lingual Fine-Tuning",
author = "Zheng, Bo and
Dong, Li and
Huang, Shaohan and
Wang, Wenhui and
Chi, Zewen and
Singhal, Saksham and
Che, Wanxiang and
Liu, Ting and
Song, Xia and
Wei, Furu",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.264/",
doi = "10.18653/v1/2021.acl-long.264",
pages = "3403--3417"
}