ConvGQR: Generative Query Reformulation for Conversational Search (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to determine a good search query from the whole conversation context are expensive and often lead to sub-optimal results. |
| Approach: | They propose a framework to reformulate conversational queries based on generative pre-trained language models (PLMs) they propose generative knowledge infusion mechanism to optimize query reformulation and retrieval. |
| Outcome: | Extensive experiments on four conversational search datasets demonstrate the effectiveness of ConvGQR. |
Similar Papers
IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance (2024.naacl-long)
Copied to clipboard
| 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. |
ConvSearch-R1: Enhancing Query Reformulation for Conversational Search with Reasoning via Reinforcement Learning (2025.emnlp-main)
Copied to clipboard
| 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. |
AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment (2025.coling-main)
Copied to clipboard
| 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. |
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning (2022.emnlp-main)
Copied to clipboard
Zeqiu Wu, Yi Luan, Hannah Rashkin, David Reitter, Hannaneh Hajishirzi, Mari Ostendorf, Gaurav Singh Tomar
| Challenge: | Existing models for conversational question answering require specific retrievers to understand user questions. |
| Approach: | They develop a query rewriting model CONQRR that rewrites a conversational question into a standalone question. |
| Outcome: | The proposed model achieves state-of-the-art on an open-domain conversational question answering dataset and is effective for two different off-the shelf retrievers. |
ConvTrans: Transforming Web Search Sessions for Conversational Dense Retrieval (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies show that conversational dense retrieval is a promising technique for realizing conversational search, but its implementation is severely hindered by the lack of data. |
| Approach: | They propose a method that transforms easily-accessible web search sessions into conversational search sessions to alleviate the data scarcity problem. |
| Outcome: | The proposed method can transform easily-accessible web search sessions into conversational search sessions. |
Explicit Query Rewriting for Conversational Dense Retrieval (2022.emnlp-main)
Copied to clipboard
| Challenge: | In a conversational search scenario, a query might be context-dependent because some words are referred to previous expressions or omitted. |
| Approach: | They propose a model that performs query rewriting and context modelling in a unified framework by highlighting relevant terms in the query context. |
| Outcome: | The proposed model outperforms baseline models in terms of quality of query rewriting and quality of contextualized query embedding. |
Multi-Task Learning of Query Generation and Classification for Generative Conversational Question Rewriting (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to identifying ambiguous questions as part of a conversation have not addressed this challenge. |
| Approach: | They propose a multi-task learning approach that uses a text generation model for question rewriting and classification. |
| Outcome: | The proposed approach outperforms single-task learning baselines on three LIF test sets. |
DVCQR: Dual-View Conversational Query Rewriting with Stage-wise Reinforcement Learning (2026.acl-long)
Copied to clipboard
| 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 . |
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent advances in conversational IR systems have seen a resurgent interest in conversation . generative query rewrite generates reconstructed query based on the conversation history . |
| Approach: | They propose to use unlabeled data to make further improvements using contrastive co-training paradigm. |
| Outcome: | The proposed model is robust to noise and language style shift under few-shot and zero-shot scenarios. |
CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in task-solving capabilities of Large Language Models (LLMs) have motivated researchers to integrate these models into existing conversational search systems. |
| Approach: | They propose a method that leverages the capabilities of large language models to resolve ambiguities in conversation history before query rewriting. |
| Outcome: | The proposed method leads to state-of-the-art results across most settings compared with closed-source LLMs. |