Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure

• URL: http://arxiv.org/abs/2203.08430v1
• Date: Wed, 16 Mar 2022 07:09:35 GMT
• Title: Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure
• Authors: Yuan Chai, Yaobo Liang, Nan Duan
• Abstract summary: We study three language properties: constituent order, composition and word co-occurrence. Our main conclusion is that the contribution of constituent order and word co-occurrence is limited, while the composition is more crucial to the success of cross-linguistic transfer.
• Score: 54.01613740115601
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multilingual pre-trained language models, such as mBERT and XLM-R, have shown impressive cross-lingual ability. Surprisingly, both of them use multilingual masked language model (MLM) without any cross-lingual supervision or aligned data. Despite the encouraging results, we still lack a clear understanding of why cross-lingual ability could emerge from multilingual MLM. In our work, we argue that cross-language ability comes from the commonality between languages. Specifically, we study three language properties: constituent order, composition and word co-occurrence. First, we create an artificial language by modifying property in source language. Then we study the contribution of modified property through the change of cross-language transfer results on target language. We conduct experiments on six languages and two cross-lingual NLP tasks (textual entailment, sentence retrieval). Our main conclusion is that the contribution of constituent order and word co-occurrence is limited, while the composition is more crucial to the success of cross-linguistic transfer.

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