EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems

[2503.23078] EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2503.23078 (cs) [Submitted on 29 Mar 2025 ( v1 ), last revised 9 Apr 2026 (this version, v3)] Title: EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems Authors: Zhengyi Zhao , Shubo Zhang , Yiming Du , Bin Liang , Baojun Wang , Zhongyang Li , Binyang Li , Kam-Fai Wong View a PDF of the paper titled EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems, by Zhengyi Zhao and 7 other authors View PDF HTML (experimental) Abstract: Large language models have improved dialogue systems, but often process conversational turns in isolation, overlooking the event structures that guide natural interactions. Hence we introduce EventWeave, a framework that explicitly models relationships between conversational events to generate more contextually appropriate dialogue responses. EventWeave constructs a dynamic event graph that distinguishes between core events (main goals) and supporting events (interconnected details), employing a multi-head attention mechanism to selectively determine which events are most relevant to the current turn. Unlike summarization or standard graph-based approaches, our method captures three distinct relationship types between events, allowing for more nuanced context modeling. Experiments on three dialogue datasets demonstrate that EventWeave produces more natural and contextually appropriate responses while requiring less computational overhead than models processing the entire dialogue history. Ablation studies confirm improvements stem from better event relationship modeling rather than increased information density. Our approach effectively balances comprehensive context understanding with generating concise responses, maintaining strong performance across various dialogue lengths through targeted optimization techniques. Comments: Accepted by ACL'26 Subjects: Computation and Language (cs.CL) Cite as: arXiv:2503.23078 [cs.CL] (or arXiv:2503.23078v3 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2503.23078 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhengyi Zhao [ view email ] [v1] Sat, 29 Mar 2025 13:33:42 UTC (3,362 KB) [v2] Fri, 21 Nov 2025 01:28:03 UTC (3,247 KB) [v3] Thu, 9 Apr 2026 09:44:36 UTC (3,250 KB) Full-text links: Access Paper: View a PDF of the paper titled EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems, by Zhengyi Zhao and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2025-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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