ICR: Iterative Clarification and Rewriting for Conversational Search

• URL: http://arxiv.org/abs/2509.05100v2
• Date: Tue, 16 Sep 2025 01:26:42 GMT
• Title: ICR: Iterative Clarification and Rewriting for Conversational Search
• Authors: Zhiyu Cao, Peifeng Li, Qiaoming Zhu,
• Abstract summary: We propose an iterative rewriting scheme that pivots on clarification questions.<n>Within this framework, the model alternates between generating clarification questions and rewritten queries.<n>Our ICR can continuously improve retrieval performance in the clarification-rewriting iterative process.
• Score: 25.48484246820614
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most previous work on Conversational Query Rewriting employs an end-to-end rewriting paradigm. However, this approach is hindered by the issue of multiple fuzzy expressions within the query, which complicates the simultaneous identification and rewriting of multiple positions. To address this issue, we propose a novel framework ICR (Iterative Clarification and Rewriting), an iterative rewriting scheme that pivots on clarification questions. Within this framework, the model alternates between generating clarification questions and rewritten queries. The experimental results show that our ICR can continuously improve retrieval performance in the clarification-rewriting iterative process, thereby achieving state-of-the-art performance on two popular datasets.

Related papers

• Chain of Retrieval: Multi-Aspect Iterative Search Expansion and Post-Order Search Aggregation for Full Paper Retrieval [68.71038700559195]
  Chain of Retrieval(COR) is a novel iterative framework for full-paper retrieval.<n>We present SCIBENCH, a benchmark providing both complete and segmented contexts of full papers for queries and candidates.
  arXiv  Detail & Related papers  (2025-07-14T08:41:53Z)
• MultiConIR: Towards multi-condition Information Retrieval [38.864056667809095]
  MultiConIR is a benchmark designed to evaluate retrieval and reranking models under nuanced multi-condition query scenarios.<n>Most retrievers and rerankers exhibit severe performance degradation as query complexity increases.<n>This work delves into the factors contributing to reranker performance deterioration and examines how condition positioning within queries affects similarity assessment.
  arXiv  Detail & Related papers  (2025-03-11T05:02:03Z)
• Exploring Rewriting Approaches for Different Conversational Tasks [63.56404271441824]
  The exact rewriting approach may often depend on the use case and application-specific tasks supported by the conversational assistant.<n>We systematically investigate two different approaches, denoted as rewriting and fusion, on two fundamentally different generation tasks.<n>Our results indicate that the specific rewriting or fusion approach highly depends on the underlying use case and generative task.
  arXiv  Detail & Related papers  (2025-02-26T06:05:29Z)
• Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search [32.35446999027349]
  We leverage both rewritten queries and relevance judgments in the conversational search data to train a better query representation model. The proposed model -- Query Representation Alignment Conversational Retriever, QRACDR, is tested on eight datasets.
  arXiv  Detail & Related papers  (2024-07-29T17:14:36Z)
• AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment [16.62505706601199]
  We present a novel framework AdaCQR for conversational search reformulation.<n>By aligning reformulation models with both term-based and semantic-based retrieval systems, AdaCQR enhances the generalizability of information-seeking queries.<n> Experimental results on the TopiOCQA and QReCC datasets demonstrate that AdaCQR outperforms the existing methods in a more efficient framework.
  arXiv  Detail & Related papers  (2024-07-02T05:50:16Z)
• A Surprisingly Simple yet Effective Multi-Query Rewriting Method for Conversational Passage Retrieval [14.389703823471574]
  We propose the use of a neural query rewriter to generate multiple queries and show how to integrate those queries in the passage retrieval pipeline efficiently. The main strength of our approach lies in its simplicity: it leverages how the beam search algorithm works and can produce multiple query rewrites at no additional cost.
  arXiv  Detail & Related papers  (2024-06-27T07:43:03Z)
• Adaptive Query Rewriting: Aligning Rewriters through Marginal Probability of Conversational Answers [66.55612528039894]
  AdaQR is a framework for training query rewriting models with limited rewrite annotations from seed datasets and completely no passage label. A novel approach is proposed to assess retriever's preference for these candidates by the probability of answers conditioned on the conversational query.
  arXiv  Detail & Related papers  (2024-06-16T16:09:05Z)
• LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback [70.44530794897861]
  Pseudo-relevance feedback (PRF) has proven to be an effective query reformulation technique to improve retrieval accuracy. Existing PRF methods independently treat revised queries originating from the same query but using different numbers of feedback documents. We propose the Loss-over-Loss (LoL) framework to compare the reformulation losses between different revisions of the same query during training.
  arXiv  Detail & Related papers  (2022-04-25T10:42:50Z)
• Query Resolution for Conversational Search with Limited Supervision [63.131221660019776]
  We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC.
  arXiv  Detail & Related papers  (2020-05-24T11:37:22Z)
• Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting [56.268862325167575]
  We tackle conversational passage retrieval (ConvPR) with query reformulation integrated into a multi-stage ad-hoc IR system. We propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, standalone, human-understandable queries with a pretrained sequence-tosequence model.
  arXiv  Detail & Related papers  (2020-05-05T14:30:20Z)
• Using Image Captions and Multitask Learning for Recommending Query Reformulations [11.99358906295761]
  We aim to enhance the query recommendation experience for a commercial image search engine. Our proposed methodology incorporates current state-of-the-art practices from relevant literature.
  arXiv  Detail & Related papers  (2020-03-02T08:22:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.