Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training

• URL: http://arxiv.org/abs/2104.08645v1
• Date: Sat, 17 Apr 2021 21:21:53 GMT
• Title: Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training
• Authors: Kuan-Hao Huang, Wasi Uddin Ahmad, Nanyun Peng, Kai-Wei Chang
• Abstract summary: We study two widely used robust training methods: adversarial training and randomized smoothing. The experimental results demonstrate that robust training can improve zero-shot cross-lingual transfer for text classification.
• Score: 45.48003947488825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, pre-trained multilingual language models, such as multilingual BERT and XLM-R, exhibit good performance on zero-shot cross-lingual transfer learning. However, since their multilingual contextual embedding spaces for different languages are not perfectly aligned, the difference between representations of different languages might cause zero-shot cross-lingual transfer failed in some cases. In this work, we draw connections between those failed cases and adversarial examples. We then propose to use robust training methods to train a robust model that can tolerate some noise in input embeddings. We study two widely used robust training methods: adversarial training and randomized smoothing. The experimental results demonstrate that robust training can improve zero-shot cross-lingual transfer for text classification. The performance improvements become significant when the distance between the source language and the target language increases.

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