\begin{abstract}
    This paper describes the \textsc{LECS Lab} submission to the AmericasNLP 2024 Shared Task on the Creation of Educational Materials for Indigenous Languages \citep{americasnlp-2024-ST2}. 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. %as well. 
    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.

\end{abstract}