\input{tables/hyperparametersMain}

\section{Related Work}
\label{sec:related-work}

Many of our strategies are inspired by research in morphological inflection. Morphological (re)inflection is the task of predicting an inflected form given a lemma or wordform and one or more target morphological features, and has been studied extensively through several shared tasks \citep{cotterell-etal-2016-sigmorphon, cotterell-etal-2017-conll, cotterell-etal-2018-conll, vylomova-etal-2020-sigmorphon, pimentel-ryskina-etal-2021-sigmorphon, kodner-etal-2022-sigmorphon, goldman-etal-2023-sigmorphon}. 

Morphological inflection has been studied with neural models such as RNNs \citep{kann-schutze-2016-single}, convolutional neural networks \citep{ostling-2016-morphological}, variational autoencoders \citep{zhou-neubig-2017-morphological}, and transformers \citep{wu-etal-2021-applying}.

Data augmentation has been proposed as a strategy to address the challenges of training neural models on inflection tasks, particularly with limited data. Approaches have included creating artificial examples that copy the inputs directly to the outputs \citep{kann-schutze-2017-unlabeled, bergmanis-etal-2017-training, liu-hulden-2022-transformer, yang-etal-2022-generalizing}, creating synthetic examples using morphological analyzers \citep{nicolai-etal-2017-cant}, and editing substrings using various methods to identify candidate stems \citep{silfverberg-etal-2017-data, anastasopoulos-neubig-2019-pushing}.