Enhancing Translation for Indigenous Languages: Experiments with
  Multilingual Models
Atnafu Lambebo Tonja Hellina Hailu Nigatu Olga Kolesnikova Grigori Sidorov Alexander Gelbukh Jugal Kalita
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.