SubmissionNumber#=%=#5
FinalPaperTitle#=%=#Modeling Orthographic Variation in Occitan's Dialects
ShortPaperTitle#=%=#
NumberOfPages#=%=#11
CopyrightSigned#=%=#
JobTitle#==#
Organization#==#
Abstract#==#Effectively normalizing spellings in 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, 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.
Author{1}{Firstname}#=%=#Zachary William
Author{1}{Lastname}#=%=#Hopton
Author{1}{Username}#=%=#zhopto3
Author{1}{Email}#=%=#zacharywilliam.hopton@uzh.ch
Author{1}{Affiliation}#=%=#University of Zurich
Author{2}{Firstname}#=%=#Noëmi
Author{2}{Lastname}#=%=#Aepli
Author{2}{Username}#=%=#naepli
Author{2}{Email}#=%=#noemi.aepli@uzh.ch
Author{2}{Affiliation}#=%=#University of Zurich

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