@inproceedings{sun-etal-2025-comet,
    title = "Comet: Dialog Context Fusion Mechanism for End-to-End Task-Oriented Dialog with Multi-task Learning",
    author = "Sun, Haipeng  and
      Bao, Junwei  and
      Wu, Youzheng  and
      He, Xiaodong",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-main.702/",
    pages = "10541--10553",
    abstract = "Existing end-to-end task-oriented dialog systems often encounter challenges arising from implicit information, coreference, and the presence of noisy and irrelevant data within the dialog context. These issues hinder the system{'}s ability to fully comprehend critical information and lead to inaccurate responses. To address these concerns, we propose Comet, a dialog context fusion mechanism for end-to-end task-oriented dialog, augmented with three supplementary tasks: dialog summarization, domain prediction, and slot detection. Dialog summarization facilitates a more comprehensive understanding of important dialog context information by Comet. Domain prediction enables Comet to concentrate on domain-specific information, thus reducing interference from irrelevant information. Slot detection empowers Comet to accurately identify and comprehend essential dialog context information. Additionally, we introduce a data refinement strategy to enhance the comprehensiveness and recommendability of the generated responses. Experimental results demonstrate the superior performance of our proposed methods compared to existing end-to-end task-oriented dialog systems, achieving state-of-the-art results on the MultiWOZ and CrossWOZ datasets."
}