Cross-Lingual Language Model Meta-Pretraining

• URL: http://arxiv.org/abs/2109.11129v1
• Date: Thu, 23 Sep 2021 03:47:44 GMT
• Title: Cross-Lingual Language Model Meta-Pretraining
• Authors: Zewen Chi, Heyan Huang, Luyang Liu, Yu Bai, Xian-Ling Mao
• Abstract summary: We propose a cross-lingual language model meta-pretraining, which learns the two abilities in different training phases. Our method improves both generalization and cross-lingual transfer, and produces better-aligned representations across different languages.
• Score: 21.591492094502424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The success of pretrained cross-lingual language models relies on two essential abilities, i.e., generalization ability for learning downstream tasks in a source language, and cross-lingual transferability for transferring the task knowledge to other languages. However, current methods jointly learn the two abilities in a single-phase cross-lingual pretraining process, resulting in a trade-off between generalization and cross-lingual transfer. In this paper, we propose cross-lingual language model meta-pretraining, which learns the two abilities in different training phases. Our method introduces an additional meta-pretraining phase before cross-lingual pretraining, where the model learns generalization ability on a large-scale monolingual corpus. Then, the model focuses on learning cross-lingual transfer on a multilingual corpus. Experimental results show that our method improves both generalization and cross-lingual transfer, and produces better-aligned representations across different languages.

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