TeCQR: A Conversational Model for Related Question Retrieval in cQA
Researchers propose TeCQR, a model for related question retrieval in community question answering (cQA) platforms like Stack Overflow. Unlike traditional static approaches, TeCQR leverages conversations to capture fine-grained question representations. It builds conversations using tag-enhanced clarifying questions and includes a noise tolerance model to evaluate semantic similarity between questions and tags, handling noisy data effectively.
Key facts
- TeCQR is a related question retrieval model using conversations.
- It targets cQA platforms like Stack Overflow.
- Traditional approaches are static and neglect interaction.
- Conversations help distinguish fine-grained question representations.
- Tag-enhanced clarifying questions (CQs) are used to build conversations.
- A noise tolerance model evaluates semantic similarity between questions and tags.
- The model handles noisy data effectively.
Entities
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