nmT5 - Is parallel data still relevant for pre-training massively multilingual language models?

Mihir Kale, Aditya Siddhant, Rami Al-Rfou, Linting Xue, Noah Constant, Melvin Johnson


Abstract
Recently, mT5 - a massively multilingual version of T5 - leveraged a unified text-to-text format to attain state-of-the-art results on a wide variety of multilingual NLP tasks. In this paper, we investigate the impact of incorporating parallel data into mT5 pre-training. We find that multi-tasking language modeling with objectives such as machine translation during pre-training is a straightforward way to improve performance on downstream multilingual and cross-lingual tasks. However, the gains start to diminish as the model capacity increases, suggesting that parallel data might not be as essential for larger models. At the same time, even at larger model sizes, we find that pre-training with parallel data still provides benefits in the limited labelled data regime
Anthology ID:
2021.acl-short.87
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
683–691
Language:
URL:
https://aclanthology.org/2021.acl-short.87
DOI:
10.18653/v1/2021.acl-short.87
Bibkey:
Cite (ACL):
Mihir Kale, Aditya Siddhant, Rami Al-Rfou, Linting Xue, Noah Constant, and Melvin Johnson. 2021. nmT5 - Is parallel data still relevant for pre-training massively multilingual language models?. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 683–691, Online. Association for Computational Linguistics.
Cite (Informal):
nmT5 - Is parallel data still relevant for pre-training massively multilingual language models? (Kale et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-short.87.pdf
Data
OPUS-100mC4