Parallel Corpus for Indigenous Language Translation: Spanish-Mazatec and
  Spanish-Mixtec
Atnafu Lambebo Tonja Christian Maldonado-Sifuentes David Alejandro Mendoza Castillo Olga Kolesnikova Noé Castro-Sánchez Grigori Sidorov Alexander Gelbukh
In this paper, we present a parallel Spanish-Mazatec and Spanish-Mixtec
corpus for machine translation (MT) tasks, where Mazatec and Mixtec are two
indigenous Mexican languages. We evaluated the usability of the collected
corpus using three different approaches: transformer, transfer learning, and
fine-tuning pre-trained multilingual MT models. Fine-tuning the Facebook
M2M100-48 model outperformed the other approaches, with BLEU scores of 12.09
and 22.25 for Mazatec-Spanish and Spanish-Mazatec translations, respectively,
and 16.75 and 22.15 for Mixtec-Spanish and Spanish-Mixtec translations,
respectively. The findings show that the dataset size (9,799 sentences in
Mazatec and 13,235 sentences in Mixtec) affects translation performance and
that indigenous languages work better when used as target languages. The
findings emphasize the importance of creating parallel corpora for indigenous
languages and fine-tuning models for low-resource translation tasks. Future
research will investigate zero-shot and few-shot learning approaches to further
improve translation performance in low-resource settings. The dataset and
scripts are available at
\url{https://github.com/atnafuatx/Machine-Translation-Resources}