This paper describes the LECS Lab submission to the AmericasNLP 2024 Shared Task on the Creation of Educational Materials for Indigenous Languages. The task requires transforming a base sentence with regards to one or more linguistic properties (such as negation or tense). We observe that this task shares many similarities with the well-studied task of word-level morphological inflection, and we explore whether the findings from inflection research are applicable to this task. In particular, we experiment with a number of augmentation strategies, finding that they can significantly benefit performance, but that not all augmented data is necessarily beneficial. Furthermore, we find that our character-level neural models show high variability with regards to performance on unseen data, and may not be the best choice when training data is limited.

In this work, we present the results of the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages of the Americas. This edition of the shared task featured eleven language pairs, one of which – Chatino-Spanish – uses a newly collected evaluation dataset, consisting of professionally translated text from the legal domain. Seven teams participated in the shared task, with a total of 181 submissions. Additionally, we conduct a human evaluation of the best system outputs, and compare them to the best submissions from the prior shared task. We find that this analysis agrees with the quantitative measures used to rank submissions, which shows further improvements of 9.64 ChrF on average across all languages, when compared to the prior winning system.