\section{Introduction}
\label{sec:introduction}
\textbf{Morphological inflection} is an NLP task with a rich history of rule-based, statistical, and neural methods (\citealt{clark2002memory, durrett-denero-2013-supervised, nicolai-etal-2015-inflection, cotterell-etal-2016-sigmorphon, faruqui-etal-2016-morphological, wu-etal-2021-applying}; inter alia). Typically, systems must predict an inflected form of a word (such as \textit{``cats''}) given a lemma form (\textit{``cat''}) and an inflectional change (plural).

In the AmericasNLP 2024 Shared Task on Creation of Educational Materials for Indigenous Languages \citep{americasnlp-2024-ST2}, systems must convert a base sentence into a target sentence by changing one or more linguistic properties (example in \autoref{tab:example}). Generally, this transformation involves inserting or deleting helper words, modifying the inflection of words in the source sentence, or both, and we observe many similarities (and some differences) between this task and the morphological inflection task.

\begin{table}[h]
    \centering
    \small
    \begin{tabular}{ l  l}
    \toprule
        \textbf{Source} & Ko'one'ex ich kool  \\ \midrule
        \textbf{Change} & PERSON:1\_PL \\ \midrule
        \textbf{Target} & Ko'ox ich kool \\
        \bottomrule
    \end{tabular}
    \caption{Example from the Yukatek Maya training data.}
    \label{tab:example}
\end{table}

In approaching this task, we apply lessons from research on inflection models. 
%to this task. 
The shared task poses particular difficulties due to the limited amount of available training data. To alleviate this issue, we utilize sequence-to-sequence (seq2seq) neural models and explore various techniques, focusing in particular on exploring various \textbf{data augmentation} strategies. We present results for all three task languages: Bribri, Yukatek Maya,\footnote{This language is referred to by task organizers (and many speakers) simply as `Maya' - we also use this shorter form.} and Guaran\'{i}. Our code is available on GitHub.\footnote{\url{https://github.com/lecs-lab/americasnlp2024}}