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New conversational search method uses iterative query rewriting and corpus feedback

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel approach to conversational search by recasting query rewriting as a sequential retrieval problem. This method allows an agent to iteratively rewrite user queries, retrieve relevant documents, and refine subsequent rewrites based on the corpus feedback. Trained using reinforcement learning without human annotations, the agent demonstrates improved performance on benchmarks like TopiOCQA and QReCC, outperforming existing baselines and showing effectiveness across different retrieval backends. AI

IMPACT This research could lead to more effective and context-aware search engines, improving user experience in conversational AI systems.

RANK_REASON Academic paper detailing a new method for conversational search. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New conversational search method uses iterative query rewriting and corpus feedback

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Academic paper detailing a new method for conversational search. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Wei Niu ·

    Learning Multi-Step Query Rewriting via Corpus Feedback for Conversational Search

    Conversational Query Rewriting (CQR) turns a context dependent user turn into a standalone query for a retriever, and most methods do this in a single step from the dialogue history before retrieving once. The rewrite is therefore fixed before any corpus evidence is available to …

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