@inproceedings{duan-etal-2025-diadp,
    title = "{D}ia{DP}@{XLLM}25: Advancing {C}hinese Dialogue Parsing via Unified Pretrained Language Models and Biaffine Dependency Scoring",
    author = "Duan, Shuoqiu  and
      Chen, Xiaoliang  and
      Miao, Duoqian  and
      Gu, Xu  and
      Li, Xianyong  and
      Du, Yajun",
    editor = "Fei, Hao  and
      Tu, Kewei  and
      Zhang, Yuhui  and
      Hu, Xiang  and
      Han, Wenjuan  and
      Jia, Zixia  and
      Zheng, Zilong  and
      Cao, Yixin  and
      Zhang, Meishan  and
      Lu, Wei  and
      Siddharth, N.  and
      {\O}vrelid, Lilja  and
      Xue, Nianwen  and
      Zhang, Yue",
    booktitle = "Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.xllm-1.22/",
    pages = "266--273",
    ISBN = "979-8-89176-286-2",
    abstract = "Dialogue-level dependency parsing is crucial for understanding complex linguistic structures in conversational data, yet progress has been hindered by limited annotated resources and inadequate modeling of dialogue dynamics. Existing methods often fail to capture both intra- and inter-utterance dependencies effectively, particularly in languages like Chinese with rich contextual interactions. To address these challenges, we propose InterParser, a novel framework that integrates a pretrained language model (PLM), bidirectional GRU (BiGRU), and biaffine scoring for comprehensive dependency parsing. Our model encodes token sequences using a PLM, refines representations via deep BiGRU layers, and employs separate projections for ``head'' and ``dependent'' roles to optimize arc and relation prediction. For cross-utterance dependencies, speaker-specific feature projections are introduced to enhance dialogue-aware scoring. Joint training minimizes cross-entropy losses for both intra- and inter-utterance dependencies, ensuring unified optimization. Experiments on a standard Chinese benchmark demonstrate that InterParser significantly outperforms prior methods, achieving state-of-the-art labeled attachment scores (LAS) for both intra- and inter-utterance parsing."
}