# A Comparison of Question Rewriting Methods for Conversational Passage Retrieval

arXiv: https://arxiv.org/abs/2101.07382 · HTML: https://mlsanity.com/paper/2101.07382/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 19 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [Open-Domain Question Answering Goes Conversational via Question Rewriting](https://mlsanity.com/paper/2010.04898/) (2020)
- [Question Rewriting for Conversational Question Answering](https://mlsanity.com/paper/2004.14652/) (2020)
- [Few-Shot Generative Conversational Query Rewriting](https://mlsanity.com/paper/2006.05009/) (2020)
- [Query Resolution for Conversational Search with Limited Supervision](https://mlsanity.com/paper/2005.11723/) (2020)
- [Topic Propagation in Conversational Search](https://mlsanity.com/paper/2004.14054/) (2020)

## Built on by (most cited first, 16 of 16)

- [ChatQA: Surpassing GPT-4 on Conversational QA and RAG](https://mlsanity.com/paper/2401.10225/) (2024)
- [ConvGQR: Generative Query Reformulation for Conversational Search](https://mlsanity.com/paper/2305.15645/) (2023)
- [Evaluating Mixed-initiative Conversational Search Systems via User Simulation](https://mlsanity.com/paper/2204.08046/) (2022)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [Post-Abstention: Towards Reliably Re-Attempting the Abstained Instances in QA](https://mlsanity.com/paper/2305.01812/) (2023)
- [Query Performance Prediction: From Ad-hoc to Conversational Search](https://mlsanity.com/paper/2305.10923/) (2023)
- [Question rewriting? Assessing its importance for conversational question answering](https://mlsanity.com/paper/2201.09146/) (2022)
- [ConvSearch-R1: Enhancing Query Reformulation for Conversational Search with Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2505.15776/) (2025)
- [Conversational Search: From Fundamentals to Frontiers in the LLM Era](https://mlsanity.com/paper/2506.10635/) (2025)
- [From Rewriting to Remembering: Common Ground for Conversational QA Models](https://mlsanity.com/paper/2204.03930/) (2022)
- [Estimating the Usefulness of Clarifying Questions and Answers for Conversational Search](https://mlsanity.com/paper/2401.11463/) (2024)
- [Towards Self-Contained Answers: Entity-Based Answer Rewriting in Conversational Search](https://mlsanity.com/paper/2403.01747/) (2024)
- [DQA: Diagnostic Question Answering for IT Support](https://mlsanity.com/paper/2604.05350/) (2026)
- [Leveraging Query Resolution and Reading Comprehension for Conversational Passage Retrieval](https://mlsanity.com/paper/2102.08795/) (2021)
- [CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite](https://mlsanity.com/paper/2403.11873/) (2024)
- [When should I search more: Adaptive Complex Query Optimization with Reinforcement Learning](https://mlsanity.com/paper/2601.21208/) (2026)

## Abstract (arXiv)

Conversational passage retrieval relies on question rewriting to modify the original question so that it no longer depends on the conversation history. Several methods for question rewriting have recently been proposed, but they were compared under different retrieval pipelines. We bridge this gap by thoroughly evaluating those question rewriting methods on the TREC CAsT 2019 and 2020 datasets under the same retrieval pipeline. We analyze the effect of different types of question rewriting methods on retrieval performance and show that by combining question rewriting methods of different types we can achieve state-of-the-art performance on both datasets.