@inproceedings{zhang-etal-2025-lcan,
    title = "{LCAN}: A Label-Aware Contrastive Attention Network for Multi-Intent Recognition and Slot Filling in Task-Oriented Dialogue Systems",
    author = "Zhang, Shuli  and
      You, Zhiqiang  and
      Qi, Xiao Xiang  and
      Liu, Peng  and
      Wu, Gaode  and
      Xia, Kan  and
      Huang, Shenguang",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1395/",
    doi = "10.18653/v1/2025.findings-emnlp.1395",
    pages = "25603--25612",
    ISBN = "979-8-89176-335-7",
    abstract = "Multi-intent utterances processing remains a persistent challenge due to intricate intent-slot dependencies and semantic ambiguities. Traditional methods struggle to model these complex interactions, particularly when handling overlapping slot structures across multiple intents. This paper introduces a label-aware contrastive attention network (LCAN), a joint modeling approach for multi-intent recognition and slot filling in task-oriented dialogue systems. LCAN addresses this issue by integrating label-aware attention and contrastive learning strategies, improving semantic understanding and generalization in multi-intent scenarios. Extensive experiments on the MixATIS and MixSNIPS datasets demonstrate LCAN{'}s superiority over existing models, achieving improved intent recognition and slot filling performance, particularly in handling overlapping or complex semantic structures in multi-intent settings."
}