Soft Language Clustering for Multilingual Model Pre-training

• URL: http://arxiv.org/abs/2306.07610v1
• Date: Tue, 13 Jun 2023 08:08:08 GMT
• Title: Soft Language Clustering for Multilingual Model Pre-training
• Authors: Jiali Zeng and Yufan Jiang and Yongjing Yin and Yi Jing and Fandong Meng and Binghuai Lin and Yunbo Cao and Jie Zhou
• Abstract summary: We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
• Score: 57.18058739931463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from source languages or when pre-training data is limited in size. In this paper, we propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods. On the tasks of XTREME including text classification, sequence labeling, question answering, and sentence retrieval, both base- and large-size language models pre-trained with our proposed method exhibit consistent performance improvement. Furthermore, it provides substantial advantages for low-resource languages in unsupervised sentence retrieval and for target languages that differ greatly from the source language in cross-lingual transfer.

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