Papers with CQR
Improving Contextual Query Rewrite for Conversational AI Agents through User-preference Feedback Learning (2023.emnlp-industry)
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| Challenge: | Contextual query rewriting (CQR) is a crucial component in Conversational AI agents, leveraging contextual information from previous user-agent conversations to improve comprehension of current user intent. |
| Approach: | They propose a framework to enhance the CQR model's capability in generating user preference-aligned rewrites. |
| Outcome: | The proposed framework improves the CQR model's ability to generate user preference-aligned rewrites. |
CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers (2022.findings-emnlp)
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| Challenge: | Existing text-to-SQL methods focus on making full use of history context, but neglect to explicitly comprehend the schema and conversational dependency. |
| Approach: | They propose a CQR-SQL that explicitly exploits schema and conversational dependency for multi-turn SQL parsing. |
| Outcome: | The proposed method exploits schema and contextual dependency for multi-turn SQL parsing. |
Search-Oriented Conversational Query Editing (2023.findings-acl)
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| Challenge: | Existing CQR models are not learned toward improving the downstream search performance . existing models generate the rewrite token-by-token from scratch . |
| Approach: | They propose a text editing-based CQR model tailored for conversational search . they propose rewrite tokens are selected from the dialogue in a non-autoregressive fashion . |
| Outcome: | The proposed model outperforms state-of-the-art models on three conversational search benchmarks while having low rewriting latency. |
IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance (2024.naacl-long)
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| Challenge: | Existing methods for conversational query reformulation depend on human annotations. |
| Approach: | They propose a method that reformulates context-dependent conversational queries without relying on human rewrites. |
| Outcome: | The proposed method shows state-of-the-art performance on two widely-used datasets. |
AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment (2025.coling-main)
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| Challenge: | Existing methods to address conversational search challenges are limited by one specific retrieval system. |
| Approach: | They propose a framework to enhance generalizability of information-seeking queries by aligning reformulation models with term-based and semantic retrieval systems. |
| Outcome: | The proposed framework outperforms existing methods in a more efficient framework. |
Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search (2026.findings-acl)
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| Challenge: | Existing approaches to rewrite ambiguous queries ignore feedback from query rewriting, passage retrieval and response generation in the rewritten process. |
| Approach: | They propose to construct self-consistent preference alignment data to generate more diverse rewritten queries. |
| Outcome: | The proposed method is effective in both in- and out-of-distribution scenarios. |
KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval (2025.emnlp-main)
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| Challenge: | Existing methods that address corpus-level context loss focus on query enrichment through structured relation representations. |
| Approach: | They propose a framework for Contextual Query Retrieval that enriches queries with contextual representations derived from a corpus-centric KG. |
| Outcome: | The proposed framework outperforms strong baselines on RAGBench and MultiHop-RAG datasets in terms of retrieval effectiveness. |
DVCQR: Dual-View Conversational Query Rewriting with Stage-wise Reinforcement Learning (2026.acl-long)
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| Challenge: | Recent approaches to improve retrieval effectiveness rely on a single rewrite . however, they often suffer from conflicting optimization signals . |
| Approach: | They propose a dual-view CQR framework that generates two complementary rewrites for each query. |
| Outcome: | Experiments show that DVCQR outperforms state-of-the-art methods on most metrics . the proposed framework generates two complementary rewrites for each query . |
ConvSearch-R1: Enhancing Query Reformulation for Conversational Search with Reasoning via Reinforcement Learning (2025.emnlp-main)
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| Challenge: | Existing approaches to conversational Query Reformulation (CQR) suffer from high dependency on external supervision from annotations or large language models and insufficient alignment between the rewriter and downstream retrievers. |
| Approach: | They propose a framework that transforms context-dependent queries into self-contained forms suitable for off-the-shelf retrievers. |
| Outcome: | The proposed framework outperforms existing methods on topiOCQA and QReCC datasets while using smaller 3B parameter models without external supervision. |