PlayGround Low Resource Machine Translation System for the 2023 AmericasNLP Shared Task

Tianrui Gu, Kaie Chen, Siqi Ouyang, Lei Li


Abstract
This paper presents PlayGround’s submission to the AmericasNLP 2023 shared task on machine translation (MT) into indigenous languages. We finetuned NLLB-600M, a multilingual MT model pre-trained on Flores-200, on 10 low-resource language directions and examined the effectiveness of weight averaging and back translation. Our experiments showed that weight averaging, on average, led to a 0.0169 improvement in the ChrF++ score. Additionally, we found that back translation resulted in a 0.008 improvement in the ChrF++ score.
Anthology ID:
2023.americasnlp-1.19
Volume:
Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Manuel Mager, Abteen Ebrahimi, Arturo Oncevay, Enora Rice, Shruti Rijhwani, Alexis Palmer, Katharina Kann
Venue:
AmericasNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
173–176
Language:
URL:
https://aclanthology.org/2023.americasnlp-1.19
DOI:
10.18653/v1/2023.americasnlp-1.19
Bibkey:
Cite (ACL):
Tianrui Gu, Kaie Chen, Siqi Ouyang, and Lei Li. 2023. PlayGround Low Resource Machine Translation System for the 2023 AmericasNLP Shared Task. In Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP), pages 173–176, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
PlayGround Low Resource Machine Translation System for the 2023 AmericasNLP Shared Task (Gu et al., AmericasNLP 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.americasnlp-1.19.pdf