@inproceedings{cross-etal-2023-glossy,
    title = "Glossy Bytes: Neural Glossing using Subword Encoding",
    author = "Cross, Ziggy  and
      Yun, Michelle  and
      Apparaju, Ananya  and
      MacCabe, Jata  and
      Nicolai, Garrett  and
      Silfverberg, Miikka",
    editor = {Nicolai, Garrett  and
      Chodroff, Eleanor  and
      Mailhot, Frederic  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.sigmorphon-1.24",
    doi = "10.18653/v1/2023.sigmorphon-1.24",
    pages = "222--229",
    abstract = "This paper presents several different neural subword modelling based approaches to interlinear glossing for seven under-resourced languages as a part of the 2023 SIGMORPHON shared task on interlinear glossing. We experiment with various augmentation and tokenization strategies for both the open and closed tracks of data. We found that while byte-level models may perform well for greater amounts of data, character based approaches remain competitive in their performance in lower resource settings.",
}

This paper presents several different neural subword modelling based approaches to interlinear glossing for seven under-resourced languages as a part of the 2023 SIGMORPHON shared task on interlinear glossing. We experiment with various augmentation and tokenization strategies for both the open and closed tracks of data. We found that while byte-level models may perform well for greater amounts of data, character based approaches remain competitive in their performance in lower resource settings.
Cross, Ziggy Yun, Michelle Apparaju, Ananya MacCabe, Jata Nicolai, Garrett Silfverberg, Miikka
Glossy Bytes: Neural Glossing using Subword Encoding