  In this paper we describe the University of Sheffield's submission to the
AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages
which comprises the translation from Spanish to eleven indigenous languages.
Our approach consists of extending, training, and ensembling different
variations of NLLB-200. We use data provided by the organizers and data from
various other sources such as constitutions, handbooks, news articles, and
backtranslations generated from monolingual data. On the dev set, our best
submission outperforms the baseline by 11% average chrF across all languages,
with substantial improvements particularly for Aymara, Guarani and Quechua. On
the test set, we achieve the highest average chrF of all the submissions, we
rank first in four of the eleven languages, and at least one of our submissions
ranks in the top 3 for all languages.
