Improving Low-Resource Languages in Pre-Trained Multilingual Language Models

Viktor Hangya, Hossain Shaikh Saadi, Alexander Fraser


Abstract
Pre-trained multilingual language models are the foundation of many NLP approaches, including cross-lingual transfer solutions. However, languages with small available monolingual corpora are often not well-supported by these models leading to poor performance. We propose an unsupervised approach to improve the cross-lingual representations of low-resource languages by bootstrapping word translation pairs from monolingual corpora and using them to improve language alignment in pre-trained language models. We perform experiments on nine languages, using contextual word retrieval and zero-shot named entity recognition to measure both intrinsic cross-lingual word representation quality and downstream task performance, showing improvements on both tasks. Our results show that it is possible to improve pre-trained multilingual language models by relying only on non-parallel resources.
Anthology ID:
2022.emnlp-main.822
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11993–12006
Language:
URL:
https://aclanthology.org/2022.emnlp-main.822
DOI:
10.18653/v1/2022.emnlp-main.822
Bibkey:
Cite (ACL):
Viktor Hangya, Hossain Shaikh Saadi, and Alexander Fraser. 2022. Improving Low-Resource Languages in Pre-Trained Multilingual Language Models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11993–12006, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Improving Low-Resource Languages in Pre-Trained Multilingual Language Models (Hangya et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.822.pdf