@article{zheng-etal-2025-stpar,
    title = "{STP}ar: A Structure-Aware Triaffine Parser for Screenplay Character Coreference Resolution",
    author = "Zheng, Li  and
      Fei, Hao  and
      Chen, Lei  and
      Li, Bobo  and
      Li, Fei  and
      Teng, Chong  and
      Zhao, Liang  and
      Ji, Donghong",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "13",
    year = "2025",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/ingest-eacl/2025.tacl-1.43/",
    doi = "10.1162/tacl.a.28",
    pages = "923--937",
    abstract = "Character Coreference Resolution in Movie Screenplays (MovieCoref) is a newly emerging task for understanding complex movie plots and character relationships. This task poses greater challenges than traditional coreference resolution, due to the intricate narrative structures and character interactions unique to screenplays. In light of these challenges, we introduce a novel approach: a Structure-aware Triaffine Parser (STPar) for the MovieCoref task. STPar combines discourse and syntactic structures in the feature encoding process, enabling comprehensive analysis of ternary relationships and complex interactions. During the pairing process, STPar utilizes a triaffine scorer to consider high-order relations between candidate mention pairs, thus enhancing its ability to capture detailed narrative structures. In addition, STPar incorporates multi-task learning, encompassing singleton and span detection tasks, to further improve coreference resolution performance. Our evaluations on the MovieCoref dataset demonstrate that STPar significantly outperforms the best baseline by 7.4{\%}, 21.5{\%}, 7.1{\%}, and 10.2{\%} in F1 scores of B3, CEAFe, LEA, and CoNLL. Further analysis highlights the benefits of integrating structural discourse and syntactic information as well as the combined approaches of triaffine and multi-task learning.1"
}