@inproceedings{gu-etal-2023-playground,
title = "{P}lay{G}round Low Resource Machine Translation System for the 2023 {A}mericas{NLP} Shared Task",
author = "Gu, Tianrui and
Chen, Kaie and
Ouyang, Siqi and
Li, Lei",
editor = "Mager, Manuel and
Ebrahimi, Abteen and
Oncevay, Arturo and
Rice, Enora and
Rijhwani, Shruti and
Palmer, Alexis and
Kann, Katharina",
booktitle = "Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.americasnlp-1.19",
doi = "10.18653/v1/2023.americasnlp-1.19",
pages = "173--176",
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.",
}
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%0 Conference Proceedings
%T PlayGround Low Resource Machine Translation System for the 2023 AmericasNLP Shared Task
%A Gu, Tianrui
%A Chen, Kaie
%A Ouyang, Siqi
%A Li, Lei
%Y Mager, Manuel
%Y Ebrahimi, Abteen
%Y Oncevay, Arturo
%Y Rice, Enora
%Y Rijhwani, Shruti
%Y Palmer, Alexis
%Y Kann, Katharina
%S Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F gu-etal-2023-playground
%X 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.
%R 10.18653/v1/2023.americasnlp-1.19
%U https://aclanthology.org/2023.americasnlp-1.19
%U https://doi.org/10.18653/v1/2023.americasnlp-1.19
%P 173-176
Markdown (Informal)
[PlayGround Low Resource Machine Translation System for the 2023 AmericasNLP Shared Task](https://aclanthology.org/2023.americasnlp-1.19) (Gu et al., AmericasNLP 2023)
ACL