Modeling Orthographic Variation in Occitan's Dialects
<|reference_start|>Modeling Orthographic Variation in Occitan's Dialects: 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.<|reference_end|>
@article{hopton2024modeling,
    title={Modeling Orthographic Variation in Occitan's Dialects},
    author={Zachary William Hopton (Language and Space Lab, University of Zurich)
  and No"emi Aepli (Department of Computational Linguistics, University of
  Zurich)},
    journal={arXiv preprint arXiv:2404.19315},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.19315},
    primaryClass={cs.CL}
}