Phylogeny-Inspired Adaptation of Multilingual Models to New Languages

Fahim Faisal, Antonios Anastasopoulos

Abstract
Large pretrained multilingual models, trained on dozens of languages, have delivered promising results due to cross-lingual learning capabilities on a variety of language tasks. Further adapting these models to specific languages, especially ones unseen during pre-training, is an important goal toward 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.
Anthology ID:
2022.aacl-main.34
Volume:
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
November
Year:
2022
Address:
Online only
Editors:
Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang
Venues:
AACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
434–452
Language:
URL:
https://preview.aclanthology.org/icon-24-ingestion/2022.aacl-main.34/
DOI:
10.18653/v1/2022.aacl-main.34
Bibkey:
Cite (ACL):
Fahim Faisal and Antonios Anastasopoulos. 2022. Phylogeny-Inspired Adaptation of Multilingual Models to New Languages. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 434–452, Online only. Association for Computational Linguistics.
Cite (Informal):
Phylogeny-Inspired Adaptation of Multilingual Models to New Languages (Faisal & Anastasopoulos, AACL-IJCNLP 2022)
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PDF:
https://preview.aclanthology.org/icon-24-ingestion/2022.aacl-main.34.pdf
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