@inproceedings{nicosia-piccinno-2022-byte,
    title = "Byte-Level Massively Multilingual Semantic Parsing",
    author = "Nicosia, Massimo  and
      Piccinno, Francesco",
    editor = "FitzGerald, Jack  and
      Rottmann, Kay  and
      Hirschberg, Julia  and
      Bansal, Mohit  and
      Rumshisky, Anna  and
      Peris, Charith  and
      Hench, Christopher",
    booktitle = "Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.mmnlu-1.3/",
    doi = "10.18653/v1/2022.mmnlu-1.3",
    pages = "25--34",
    abstract = "Token free approaches have been successfully applied to a series of word and span level tasks. In this work, we evaluate a byte-level sequence to sequence model (ByT5) on the 51 languages in the MASSIVE multilingual semantic parsing dataset. We examine multiple experimental settings: (i) zero-shot, (ii) full gold data and (iii) zero-shot with synthetic data. By leveraging a state-of-the-art label projection method for machine translated examples, we are able to reduce the gap in exact match to only 5 points with respect to a model trained on gold data from all the languages. We additionally provide insights on the cross-lingual transfer of ByT5 and show how the model compares with respect to mT5 across all parameter sizes."
}