Modeling Orthographic Variation in Occitan's Dialects

Autor:	Hopton, Zachary William, Aepli, Noëmi
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Effectively normalizing textual data poses a considerable challenge, especially for low-resource languages lacking standardized writing systems. In this study, we fine-tuned a multilingual model with data from several Occitan dialects and conducted a series of experiments to assess the model's representations of these dialects. For evaluation purposes, we compiled a parallel lexicon encompassing four Occitan dialects. Intrinsic evaluations of the model's embeddings revealed that surface similarity between the dialects strengthened representations. When the model was further fine-tuned for part-of-speech tagging and Universal Dependency parsing, its performance was robust to dialectical variation, even when trained solely on part-of-speech data from a single dialect. Our findings suggest that large multilingual models minimize the need for spelling normalization during pre-processing. Comment: Accepted at VarDial 2024: The Eleventh Workshop on NLP for Similar Languages, Varieties and Dialects
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.19315 Zobrazit plný text záznamu View this record from Arxiv