PlayGround Low Resource Machine Translation System for the 2023 AmericasNLP Shared Task
Tianrui Gu, Kaie Chen, Siqi Ouyang, Lei Li
Third Workshop on Natural Language Processing for Indigenous Languages of the Americas Shared task system description Paper
TLDR:
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
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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.