@inproceedings{prazak-etal-2021-multilingual,
    title = "Multilingual Coreference Resolution with Harmonized Annotations",
    author = "Pra{\v{z}}{\'a}k, Ond{\v{r}}ej  and
      Konop{\'i}k, Miloslav  and
      Sido, Jakub",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
    month = sep,
    year = "2021",
    address = "Held Online",
    publisher = "INCOMA Ltd.",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.ranlp-1.125/",
    pages = "1119--1123",
    abstract = "In this paper, we present coreference resolution experiments with a newly created multilingual corpus CorefUD (Nedoluzhko et al.,2021). We focus on the following languages: Czech, Russian, Polish, German, Spanish, and Catalan. In addition to monolingual experiments, we combine the training data in multilingual experiments and train two joined models - for Slavic languages and for all the languages together. We rely on an end-to-end deep learning model that we slightly adapted for the CorefUD corpus. Our results show that we can profit from harmonized annotations, and using joined models helps significantly for the languages with smaller training data."
}