Beyond Static: Related Questions Retrieval Through Conversations in Community Question Answering
The paper introduces TeCQR, a model for related question retrieval in community question answering platforms that leverages conversational interactions and tag-enhanced clarifying questions to improve retrieval accuracy. Unlike static methods, TeCQR incorporates dynamic feedback and fine-grained representations through a two-stage training process. The model includes a noise tolerance mechanism to handle irrelevant responses and better align questions with tags. Experiments show TeCQR outperforms existing state-of-the-art approaches.
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Computer Science > Information Retrieval arXiv:2604.22759 (cs) [Submitted on 9 Mar 2026] Title:Beyond Static: Related Questions Retrieval Through Conversations in Community Question Answering Authors:Xiao Ao, Jie Zou, Yibiao Wei, Peng Wang, Weikang Guo View a PDF of the paper titled Beyond Static: Related Questions Retrieval Through Conversations in Community Question Answering, by Xiao Ao and 4 other authors View PDF HTML (experimental) Abstract:In community question answering (cQA) platforms like Stack Overflow, related question retrieval is recognized as a fundamental task that allows users to retrieve related questions to answer user queries automatically.
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