PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining

Machel Reid, Mikel Artetxe


Abstract
Despite the success of multilingual sequence-to-sequence pretraining, most existing approaches rely on monolingual corpora and do not make use of the strong cross-lingual signal contained in parallel data. In this paper, we present PARADISE (PARAllel &Denoising Integration in SEquence-to-sequence models), which extends the conventional denoising objective used to train these models by (i) replacing words in the noised sequence according to a multilingual dictionary, and (ii) predicting the reference translation according to a parallel corpus instead of recovering the original sequence. Our experiments on machine translation and cross-lingual natural language inference show an average improvement of 2.0 BLEU points and 6.7 accuracy points from integrating parallel data into pretraining, respectively, obtaining results that are competitive with several popular models at a fraction of their computational cost.
Anthology ID:
2022.naacl-main.58
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
800–810
Language:
URL:
https://aclanthology.org/2022.naacl-main.58
DOI:
10.18653/v1/2022.naacl-main.58
Bibkey:
Cite (ACL):
Machel Reid and Mikel Artetxe. 2022. PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 800–810, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining (Reid & Artetxe, NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.58.pdf
Code
 machelreid/paradise
Data
PAWS-XXNLI