Claire Post


2024

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On the Robustness of Neural Models for Full Sentence Transformation
Michael Ginn | Ali Marashian | Bhargav Shandilya | Claire Post | Enora Rice | Juan Vásquez | Marie Mcgregor | Matthew Buchholz | Mans Hulden | Alexis Palmer
Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024)

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.