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    <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.</abstract>
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%0 Conference Proceedings
%T Comet: Dialog Context Fusion Mechanism for End-to-End Task-Oriented Dialog with Multi-task Learning
%A Sun, Haipeng
%A Bao, Junwei
%A Wu, Youzheng
%A He, Xiaodong
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F sun-etal-2025-comet
%X 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.
%U https://aclanthology.org/2025.coling-main.702/
%P 10541-10553

Markdown (Informal)

[Comet: Dialog Context Fusion Mechanism for End-to-End Task-Oriented Dialog with Multi-task Learning](https://aclanthology.org/2025.coling-main.702/) (Sun et al., COLING 2025)

• Comet: Dialog Context Fusion Mechanism for End-to-End Task-Oriented Dialog with Multi-task Learning (Sun et al., COLING 2025)

ACL

• Haipeng Sun, Junwei Bao, Youzheng Wu, and Xiaodong He. 2025. Comet: Dialog Context Fusion Mechanism for End-to-End Task-Oriented Dialog with Multi-task Learning. In Proceedings of the 31st International Conference on Computational Linguistics, pages 10541–10553, Abu Dhabi, UAE. Association for Computational Linguistics.