Leveraging Synthetic Targets for Machine Translation

Sarthak Mittal, Oleksii Hrinchuk, Oleksii Kuchaiev


Abstract
In this work, we provide a recipe for training machine translation models in a limited resource setting by leveraging synthetic target data generated using a large pre-trained model. We show that consistently across different benchmarks in bilingual, multilingual, and speech translation setups, training models on synthetic targets outperforms training on the actual ground-truth data. This performance gap grows bigger with increasing limits on the amount of available resources in the form of the size of the dataset and the number of parameters in the model. We also provide preliminary analysis into whether this boost in performance is linked to ease of optimization or more deterministic nature of the predictions, and whether this paradigm leads to better out-of-distribution performance across different testing domains.
Anthology ID:
2023.findings-acl.597
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9365–9379
Language:
URL:
https://aclanthology.org/2023.findings-acl.597
DOI:
10.18653/v1/2023.findings-acl.597
Bibkey:
Cite (ACL):
Sarthak Mittal, Oleksii Hrinchuk, and Oleksii Kuchaiev. 2023. Leveraging Synthetic Targets for Machine Translation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 9365–9379, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Leveraging Synthetic Targets for Machine Translation (Mittal et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-acl.597.pdf