Phylogeny-Inspired Adaptation of Multilingual Models to New Languages

• URL: http://arxiv.org/abs/2205.09634v1
• Date: Thu, 19 May 2022 15:49:19 GMT
• Title: Phylogeny-Inspired Adaptation of Multilingual Models to New Languages
• Authors: Fahim Faisal, Antonios Anastasopoulos
• Abstract summary: We show how we can use language phylogenetic information to improve cross-lingual transfer leveraging closely related languages. We perform adapter-based training on languages from diverse language families (Germanic, Uralic, Tupian, Uto-Aztecan) and evaluate on both syntactic and semantic tasks.
• Score: 43.62238334380897
• License: http://creativecommons.org/licenses/by/4.0/
• 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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