@inproceedings{de-langhe-etal-2023-leveraging,
    title = "Leveraging Structural Discourse Information for Event Coreference Resolution in {D}utch",
    author = "De Langhe, Loic  and
      De Clercq, Orphee  and
      Hoste, Veronique",
    editor = "Strube, Michael  and
      Braud, Chloe  and
      Hardmeier, Christian  and
      Li, Junyi Jessy  and
      Loaiciga, Sharid  and
      Zeldes, Amir",
    booktitle = "Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.codi-1.5",
    doi = "10.18653/v1/2023.codi-1.5",
    pages = "48--53",
    abstract = "We directly embed easily extractable discourse structure information (subsection, paragraph and text type) in a transformer-based Dutch event coreference resolution model in order to more explicitly provide it with structural information that is known to be important in coreferential relationships. Results show that integrating this type of knowledge leads to a significant improvement in CONLL F1 for within-document settings (+ 8.6{\textbackslash}{\%}) and a minor improvement for cross-document settings (+ 1.1{\textbackslash}{\%}).",
}

We directly embed easily extractable discourse structure information (subsection, paragraph and text type) in a transformer-based Dutch event coreference resolution model in order to more explicitly provide it with structural information that is known to be important in coreferential relationships. Results show that integrating this type of knowledge leads to a significant improvement in CONLL F1 for within-document settings (+ 8.6{\textbackslash}{\%}) and a minor improvement for cross-document settings (+ 1.1{\textbackslash}{\%}).
De Langhe, Loic De Clercq, Orphee Hoste, Veronique
Leveraging Structural Discourse Information for Event Coreference Resolution in Dutch