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arXiv · 2205.09634

Phylogeny-Inspired Adaptation of Multilingual Models to New Languages

Fahim Faisal·Antonios Anastasopoulos
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Abstract

Large pretrained multilingual models, trained on dozens of languages, have delivered promising results due to cross-lingual learning capabilities on variety of language tasks. Further adapting these models to specific languages, especially ones unseen during pre-training, is an important goal towards expanding the coverage of language technologies. In this study, we show how we can use language phylogenetic information to improve cross-lingual transfer leveraging closely related languages in a structured, linguistically-informed manner. We perform adapter-based training on languages from diverse language families (Germanic, Uralic, Tupian, Uto-Aztecan) and evaluate on both syntactic and semantic tasks, obtaining more than 20% relative performance improvements over strong commonly used baselines, especially on languages unseen during pre-training.

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Fahim Faisal, Antonios Anastasopoulos. 2022-05-19. Phylogeny-Inspired Adaptation of Multilingual Models to New Languages. https://arxiv.org/abs/2205.09634

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