@inproceedings{de-langhe-etal-2024-enhancing,
    title = "Enhancing Unrestricted Cross-Document Event Coreference with Graph Reconstruction Networks",
    author = "de Langhe, Loic  and
      de Clercq, Orphee  and
      Hoste, Veronique",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.541",
    pages = "6122--6133",
    abstract = "Event Coreference Resolution remains a challenging discourse-oriented task within the domain of Natural Language Processing. In this paper we propose a methodology where we combine traditional mention-pair coreference models with a lightweight and modular graph reconstruction algorithm. We show that building graph models on top of existing mention-pair models leads to improved performance for both a wide range of baseline mention-pair algorithms as well as a recently developed state-of-the-art model and this at virtually no added computational cost. Moreover, additional experiments seem to indicate that our method is highly robust in low-data settings and that its performance scales with increases in performance for the underlying mention-pair models.",
}

Event Coreference Resolution remains a challenging discourse-oriented task within the domain of Natural Language Processing. In this paper we propose a methodology where we combine traditional mention-pair coreference models with a lightweight and modular graph reconstruction algorithm. We show that building graph models on top of existing mention-pair models leads to improved performance for both a wide range of baseline mention-pair algorithms as well as a recently developed state-of-the-art model and this at virtually no added computational cost. Moreover, additional experiments seem to indicate that our method is highly robust in low-data settings and that its performance scales with increases in performance for the underlying mention-pair models.
de Langhe, Loic de Clercq, Orphee Hoste, Veronique
Enhancing Unrestricted Cross-Document Event Coreference with Graph Reconstruction Networks