Embedding structure matters: Comparing methods to adapt multilingual vocabularies to new languages

• URL: http://arxiv.org/abs/2309.04679v2
• Date: Thu, 26 Oct 2023 21:26:16 GMT
• Title: Embedding structure matters: Comparing methods to adapt multilingual vocabularies to new languages
• Authors: C.M. Downey, Terra Blevins, Nora Goldfine, Shane Steinert-Threlkeld
• Abstract summary: Pre-trained multilingual language models underpin a large portion of modern NLP tools outside of English. We propose several simple techniques to replace a cross-lingual vocabulary with a compact, language-specific one.
• Score: 20.17308477850864
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Pre-trained multilingual language models underpin a large portion of modern NLP tools outside of English. A strong baseline for specializing these models for specific languages is Language-Adaptive Pre-Training (LAPT). However, retaining a large cross-lingual vocabulary and embedding matrix comes at considerable excess computational cost during adaptation. In this study, we propose several simple techniques to replace a cross-lingual vocabulary with a compact, language-specific one. Namely, we address strategies for re-initializing the token embedding matrix after vocabulary specialization. We then provide a systematic experimental comparison of our techniques, in addition to the recently-proposed Focus method. We demonstrate that: 1) Embedding-replacement techniques in the monolingual transfer literature are inadequate for adapting multilingual models. 2) Replacing cross-lingual vocabularies with smaller specialized ones provides an efficient method to improve performance in low-resource languages. 3) Simple embedding re-initialization techniques based on script-wise sub-distributions rival techniques such as Focus, which rely on similarity scores obtained from an auxiliary model.

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