Enhancing Translation for Indigenous Languages: Experiments with Multilingual Models
This work addresses the problem of low-resource machine translation for indigenous language communities, though it appears incremental in nature.
This paper tackled machine translation for 11 indigenous American languages by experimenting with multilingual models (M2M-100, mBART50) and a bilingual model, finding that the mBART setup improved upon the baseline for 3 out of 11 languages.
This paper describes CIC NLP's submission to the AmericasNLP 2023 Shared Task on machine translation systems for indigenous languages of the Americas. We present the system descriptions for three methods. We used two multilingual models, namely M2M-100 and mBART50, and one bilingual (one-to-one) -- Helsinki NLP Spanish-English translation model, and experimented with different transfer learning setups. We experimented with 11 languages from America and report the setups we used as well as the results we achieved. Overall, the mBART setup was able to improve upon the baseline for three out of the eleven languages.