---
title: "CONQRR — paper by Wu et al. (2022) — summary and ideas"
description: "Open-domain conversational question answering (CQA) needs passage retrieval for questions that… 40 references, cited by 51 papers here."
authors: ["Zeqiu Wu", "Yi Luan", "Hannah Rashkin", "David Reitter", "Hannaneh Hajishirzi", "Mari Ostendorf", "Gaurav Singh Tomar"]
year: 2022
venue: "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2022.emnlp-main.679/"
doi: "10.18653/v1/2022.emnlp-main.679"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wu-2022-conqrr-conversational-query-rewriting-retrieval/"
---
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# CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning

**Authors:** [Zeqiu Wu](https://md.research.elamur.ai/free/en/authors/zeqiu-wu/), [Yi Luan](https://md.research.elamur.ai/free/en/authors/yi-luan/), [Hannah Rashkin](https://md.research.elamur.ai/free/en/authors/hannah-rashkin/), [David Reitter](https://md.research.elamur.ai/free/en/authors/david-reitter/), [Hannaneh Hajishirzi](https://md.research.elamur.ai/free/en/authors/hannaneh-hajishirzi/), [Mari Ostendorf](https://md.research.elamur.ai/free/en/authors/mari-ostendorf/) and [Gaurav Singh Tomar](https://md.research.elamur.ai/free/en/authors/gaurav-singh-tomar/) · **Year:** 2022 · **Venue:** [Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2022](https://md.research.elamur.ai/free/en/venues/emnlp/2022/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.emnlp-main.679/) · [PDF](https://aclanthology.org/2022.emnlp-main.679.pdf) · [DOI 10.18653/v1/2022.emnlp-main.679](https://doi.org/10.18653/v1/2022.emnlp-main.679) · [OpenAlex](https://openalex.org/W4385573600)

**Citations:** 44 citations (OpenAlex, 2026-10-14) · [51](https://md.research.elamur.ai/free/en/papers/wu-2022-conqrr-conversational-query-rewriting-retrieval/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Open-domain conversational question answering (CQA) needs passage retrieval for questions that only make sense in light of earlier turns. Retrainable conversational retrievers exist, but many deployed systems, such as search engines, are black boxes that cannot be fine-tuned. The authors address this with CONQRR, which turns a context-dependent question into a self-contained query for any fixed retriever. Instead of only imitating human rewrites, CONQRR is trained with reinforcement learning on top of T5. Its reward estimates retrieval quality against the passages in the current training batch. The only supervision comes from weak retrieval signals (heuristic gold passages), optionally combined with the cross-entropy loss used by the supervised T5QR baseline.

The study also shows that human rewrites sometimes omit context the retriever would benefit from. On QReCC, CONQRR sets a new state of the art for two off-the-shelf retrievers, BM25 and a neural dual encoder. It also holds up on out-of-domain conversations and works without any human rewrite supervision. This makes it practical when the retrieval backend cannot be changed.

## Key points
- **Results:** On QReCC, CONQRR beats prior query-rewriting models by over 12% with BM25 and over 14% with the dual encoder (Ni et al., 2021), averaged over three retrieval metrics. Gains hold on all three conversation-source subsets, including one that appears only in the test set.
- **Variants:** CONQRR (RL) uses no human rewrites and still beats fully supervised baselines. CONQRR (mix), which adds cross-entropy loss, is more robust to out-of-domain dialogues and topic shifts.
- **Error analysis:** The authors checked 50 cases where CONQRR (mix) ranked gold passages worse than human rewrites. In 56% the rewrite carried less context, and in 34% it misread the user's request.
- **Cost:** Training takes about 9 hours, compared with 2 hours for fine-tuning a similar-size dual encoder. However, fine-tuning a retriever also requires re-indexing passages, which takes about 24 hours.
- **Limitations:** If the retriever can be fine-tuned, CONQRR (mix) falls behind simply feeding in the raw dialogue context. The method has only been tested on dialogues that alternate questions and answers, without clarification turns.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Zeqiu Wu, Yi Luan, Hannah Rashkin, David Reitter, Hannaneh Hajishirzi, Mari Ostendorf and Gaurav Singh Tomar. 2022. CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wu2022conqrr,
  title = {{CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning}},
  author = {Zeqiu Wu, Yi Luan, Hannah Rashkin, David Reitter, Hannaneh Hajishirzi, Mari Ostendorf and Gaurav Singh Tomar},
  booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
  year = {2022},
  doi = {10.18653/v1/2022.emnlp-main.679},
  url = {https://doi.org/10.18653/v1/2022.emnlp-main.679}
}
```

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## References

40 references, 17 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Leonard Adolphs, Benjamin Boerschinger, Christian Buck, Michelle Chen Huebscher, Massimiliano Ciaramita, Lasse Espeholt, Thomas Hofmann, and Yannic Kilcher. 2021. Boosting search engines with interactive agents. arXiv preprint arXiv:2109.00527. — [arXiv:2109.00527](https://arxiv.org/abs/2109.00527) [find](https://scholar.google.com/scholar?q=Leonard+Adolphs%2C+Benjamin+Boerschinger%2C+Christian+Buck%2C+Michelle+Chen+Huebscher%2C+Massimiliano+Ciaramita%2C+Lasse+Espeholt%2C+Thomas+Hofmann%2C+and+Yannic+Kilcher%2E+2021%2E+Boosting+search+engines+with+interact)
2. Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, and Srinivas Chappidi. 2021. [Open-domain question answering goes conversational via question rewriting](https://md.research.elamur.ai/free/en/papers/anantha-2021-open-domain-question-answering-goes/). In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguist…
3. Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, and Tong Wang. 2016. [Ms marco: A human generated machine reading comprehension dataset](https://md.research.elamur.ai/free/en/papers/bajaj-2016-ms-marco-human-generated/). arXiv preprint a…
4. Nicholas J. Belkin, Colleen Cool, Adelheit Stein, and Ulrich Thiel. 1995. Cases, scripts, and information-seeking strategies: On the design of interactive information retrieval systems. Expert Systems with Applications, 9(3):379–395. [find](https://scholar.google.com/scholar?q=Nicholas+J%2E+Belkin%2C+Colleen+Cool%2C+Adelheit+Stein%2C+and+Ulrich+Thiel%2E+1995%2E+Cases%2C+scripts%2C+and+information%2Dseeking+strategies%3A+On+the+design+of+interactive+information+retrieval+systems%2E+Expert+Systems)
5. Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, and Wei Wang. 2018. Ask the right questions: Active question reformulation with reinforcement learning. In International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Christian+Buck%2C+Jannis+Bulian%2C+Massimiliano+Ciaramita%2C+Wojciech+Gajewski%2C+Andrea+Gesmundo%2C+Neil+Houlsby%2C+and+Wei+Wang%2E+2018%2E+Ask+the+right+questions%3A+Active+question+reformulation+with+reinforcement+l)
6. Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, and Yejin Choi. 2018. [Deep communicating agents for abstractive summarization](https://md.research.elamur.ai/free/en/papers/celikyilmaz-2018-deep-communicating-agents-abstractive-summarization/). In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), p…
7. Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. 2018. [QuAC: Question answering in context](https://md.research.elamur.ai/free/en/papers/choi-2018-quac-question-answering-context/). In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2174–2184, Brussels, Belgium. Association for Computa…
8. Eunsol Choi, Jennimaria Palomaki, Matthew Lamm, Tom Kwiatkowski, Dipanjan Das, and Michael Collins. 2021. [Decontextualization: Making Sentences Stand-Alone](https://md.research.elamur.ai/free/en/papers/choi-2021-decontextualization-making-sentences-stand-alone/). Transactions of the Association for Computational Linguistics, 9:447–461.
9. Jeffrey Dalton, Chenyan Xiong, Vaibhav Kumar, and Jamie Callan. 2020. Cast-19: A dataset for conversational information seeking. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’20, page 1985–1988, New York, NY, USA. Associati… [find](https://scholar.google.com/scholar?q=Jeffrey+Dalton%2C+Chenyan+Xiong%2C+Vaibhav+Kumar%2C+and+Jamie+Callan%2E+2020%2E+Cast%2D19%3A+A+dataset+for+conversational+information+seeking%2E+In+Proceedings+of+the+43rd+International+ACM+SIGIR+Conference+on+Resear)
10. Ahmed Elgohary, Denis Peskov, and Jordan Boyd-Graber. 2019. [Can you unpack that? learning to rewrite questions-in-context](https://md.research.elamur.ai/free/en/papers/elgohary-2019-can-you-unpack-that-learning/). In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP…
11. Adam Fisch, Kenton Lee, Ming-Wei Chang, Jonathan Clark, and Regina Barzilay. 2020. CapWAP: Image captioning with a purpose. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8755–8768, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Adam+Fisch%2C+Kenton+Lee%2C+Ming%2DWei+Chang%2C+Jonathan+Clark%2C+and+Regina+Barzilay%2E+2020%2E+CapWAP%3A+Image+captioning+with+a+purpose%2E+In+Proceedings+of+the+2020+Conference+on+Empirical+Methods+in+Natural+Langua)
12. Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020. Retrieval augmented language model pre-training. In Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 3929–3938. PMLR. [find](https://scholar.google.com/scholar?q=Kelvin+Guu%2C+Kenton+Lee%2C+Zora+Tung%2C+Panupong+Pasupat%2C+and+Mingwei+Chang%2E+2020%2E+Retrieval+augmented+language+model+pre%2Dtraining%2E+In+Proceedings+of+the+37th+International+Conference+on+Machine+Learning%2C+)
13. Etsuko Ishii, Yan Xu, Samuel Cahyawijaya, and Bryan Wilie. 2022. Can question rewriting help conversational question answering? In Proceedings of the Third Workshop on Insights from Negative Results in NLP, pages 94–99, Dublin, Ireland. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Etsuko+Ishii%2C+Yan+Xu%2C+Samuel+Cahyawijaya%2C+and+Bryan+Wilie%2E+2022%2E+Can+question+rewriting+help+conversational+question+answering%3F+In+Proceedings+of+the+Third+Workshop+on+Insights+from+Negative+Results+i)
14. Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. [Dense passage retrieval for open-domain question answering](https://md.research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/). In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769– 6781, Onl…
15. Sungdong Kim and Gangwoo Kim. 2022. Saving dense retriever from shortcut dependency in conversational search. arXiv preprint arXiv:2202.07280. [find](https://scholar.google.com/scholar?q=Sungdong+Kim+and+Gangwoo+Kim%2E+2022%2E+Saving+dense+retriever+from+shortcut+dependency+in+conversational+search%2E+arXiv+preprint+arXiv%3A2202%2E07280%2E)
16. Vaibhav Kumar and Jamie Callan. 2020. Making information seeking easier: An improved pipeline for conversational search. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3971–3980, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Vaibhav+Kumar+and+Jamie+Callan%2E+2020%2E+Making+information+seeking+easier%3A+An+improved+pipeline+for+conversational+search%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2020%2C+pages)
17. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. [Exploring the limits of transfer learning with a unified text-to-text transformer](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/). Journal of Machine Learning Research, 21(140):1–67.
18. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, and Chris Alberti et al. 2019. [Natural questions: A benchmark for question answering research](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/). Transactions of the Association for Computational Linguistics, 7:452– 466.
19. Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2016. [Sequence level training with recurrent neural networks](https://md.research.elamur.ai/free/en/papers/ranzato-2015-sequence-level-training-recurrent-neural/). In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings.
20. Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. 2021. Contextualized query embeddings for conversational search. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1004–1015, Online and Punta Cana, Dominican Republic. Association for Computational Lingu… [find](https://scholar.google.com/scholar?q=Sheng%2DChieh+Lin%2C+Jheng%2DHong+Yang%2C+and+Jimmy+Lin%2E+2021%2E+Contextualized+query+embeddings+for+conversational+search%2E+In+Proceedings+of+the+2021+Conference+on+Empirical+Methods+in+Natural+Language+Process)
21. Siva Reddy, Danqi Chen, and Christopher D. Manning. 2019. [CoQA: A conversational question answering challenge](https://md.research.elamur.ai/free/en/papers/reddy-2019-coqa-conversational-question-answering-challenge/). Transactions of the Association for Computational Linguistics, 7:249–266.
22. Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, and Jimmy Lin. 2020. Conversational question reformulation via sequence-to-sequence architectures and pretrained language models. arXiv preprint arXiv:2004.01909. — [arXiv:2004.01909](https://arxiv.org/abs/2004.01909) [find](https://scholar.google.com/scholar?q=Sheng%2DChieh+Lin%2C+Jheng%2DHong+Yang%2C+Rodrigo+Nogueira%2C+Ming%2DFeng+Tsai%2C+Chuan%2DJu+Wang%2C+and+Jimmy+Lin%2E+2020%2E+Conversational+question+reformulation+via+sequence%2Dto%2Dsequence+architectures+and+pretrained+lang)
23. Yi Luan, Jacob Eisenstein, Kristina Toutanova, and Michael Collins. 2021. [Sparse, dense, and attentional representations for text retrieval](https://md.research.elamur.ai/free/en/papers/luan-2021-sparse-dense-attentional-representations-text/). Transactions of the Association for Computational Linguistics, 9:329–345.
24. Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Y. Zhao, Yi Luan, Keith B. Hall, Ming-Wei Chang, and Yinfei Yang. 2021. [Large dual encoders are generalizable retrievers](https://md.research.elamur.ai/free/en/papers/ni-2021-large-dual-encoders-generalizable-retrievers/). arXiv preprint arXiv:2112.07899.
25. Rodrigo Nogueira and Kyunghyun Cho. 2017. Task-oriented query reformulation with reinforcement learning. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 574–583, Copenhagen, Denmark. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Rodrigo+Nogueira+and+Kyunghyun+Cho%2E+2017%2E+Task%2Doriented+query+reformulation+with+reinforcement+learning%2E+In+Proceedings+of+the+2017+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+page)
26. Romain Paulus, Caiming Xiong, and Richard Socher. 2018. [A deep reinforced model for abstractive summarization](https://md.research.elamur.ai/free/en/papers/paulus-2017-deep-reinforced-model-abstractive-summarization/). In International Conference on Learning Representations.
27. Chen Qu, Liu Yang, Cen Chen, Minghui Qiu, W. Bruce Croft, and Mohit Iyyer. 2020. Open-retrieval conversational question answering. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’20, page 539–548, New York, NY, USA. Associati… [find](https://scholar.google.com/scholar?q=Chen+Qu%2C+Liu+Yang%2C+Cen+Chen%2C+Minghui+Qiu%2C+W%2E+Bruce+Croft%2C+and+Mohit+Iyyer%2E+2020%2E+Open%2Dretrieval+conversational+question+answering%2E+In+Proceedings+of+the+43rd+International+ACM+SIGIR+Conference+on+Rese)
28. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog. Accessed 22 March 2021. [find](https://scholar.google.com/scholar?q=Alec+Radford%2C+Jeffrey+Wu%2C+Rewon+Child%2C+David+Luan%2C+Dario+Amodei%2C+and+Ilya+Sutskever%2E+2019%2E+Language+models+are+unsupervised+multitask+learners%2E+OpenAI+Blog%2E+Accessed+22+March+2021%2E)
29. Steven J. Rennie, Etienne Marcheret, Youssef Mroueh, Jerret Ross, and Vaibhava Goel. 2017. [Self-critical sequence training for image captioning](https://md.research.elamur.ai/free/en/papers/rennie-2016-self-critical-sequence-training-image/). In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1179–1195.
30. Noam Shazeer and Mitchell Stern. 2018. Adafactor: Adaptive learning rates with sublinear memory cost. In Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 4596–4604. PMLR. [find](https://scholar.google.com/scholar?q=Noam+Shazeer+and+Mitchell+Stern%2E+2018%2E+Adafactor%3A+Adaptive+learning+rates+with+sublinear+memory+cost%2E+In+Proceedings+of+the+35th+International+Conference+on+Machine+Learning%2C+volume+80+of+Proceedings+)
31. Paul Solomon. 1997. Conversation in information-seeking contexts: A test of an analytical framework. Library & Information Science Research, 19(3):217– 248. [find](https://scholar.google.com/scholar?q=Paul+Solomon%2E+1997%2E+Conversation+in+information%2Dseeking+contexts%3A+A+test+of+an+analytical+framework%2E+Library+%26+Information+Science+Research%2C+19%283%29%3A217%E2%80%93+248%2E)
32. Svitlana Vakulenko, Shayne Longpre, Zhucheng Tu, and Raviteja Anantha. 2021. Question rewriting for conversational question answering. In WSDM. [find](https://scholar.google.com/scholar?q=Svitlana+Vakulenko%2C+Shayne+Longpre%2C+Zhucheng+Tu%2C+and+Raviteja+Anantha%2E+2021%2E+Question+rewriting+for+conversational+question+answering%2E+In+WSDM%2E)
33. Ellen M. Voorhees and Dawn M. Tice. 2000. [The TREC-8 question answering track](https://md.research.elamur.ai/free/en/papers/voorhees-2000-trec-8-question-answering-track/). In Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00), Athens, Greece. European Language Resources Association (ELRA).
34. Nikos Voskarides, Dan Li, Pengjie Ren, Evangelos Kanoulas, and Maarten de Rijke. 2020. Query resolution for conversational search with limited supervision. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM. [find](https://scholar.google.com/scholar?q=Nikos+Voskarides%2C+Dan+Li%2C+Pengjie+Ren%2C+Evangelos+Kanoulas%2C+and+Maarten+de+Rijke%2E+2020%2E+Query+resolution+for+conversational+search+with+limited+supervision%2E+In+Proceedings+of+the+43rd+International+ACM)
35. Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, and Wolfgang Macherey et al. 2016. Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144. [find](https://scholar.google.com/scholar?q=Yonghui+Wu%2C+Mike+Schuster%2C+Zhifeng+Chen%2C+Quoc+V%2E+Le%2C+Mohammad+Norouzi%2C+and+Wolfgang+Macherey+et+al%2E+2016%2E+Google%E2%80%99s+neural+machine+translation+system%3A+Bridging+the+gap+between+human+and+machine+transla)
36. Zeqiu Wu, Michel Galley, Chris Brockett, Yizhe Zhang, and Bill Dolan. 2021. Automatic document sketching: Generating drafts from analogous texts. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 2102–2113, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Zeqiu+Wu%2C+Michel+Galley%2C+Chris+Brockett%2C+Yizhe+Zhang%2C+and+Bill+Dolan%2E+2021%2E+Automatic+document+sketching%3A+Generating+drafts+from+analogous+texts%2E+In+Findings+of+the+Association+for+Computational+Lingu)
37. Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N. Bennett, Junaid Ahmed, and Arnold Overwijk. 2021. [Approximate nearest neighbor negative contrastive learning for dense text retrieval](https://md.research.elamur.ai/free/en/papers/xiong-2020-approximate-nearest-neighbor-negative-contrastive/). In International Conference on Learning Representations.
38. Peilin Yang, Hui Fang, and Jimmy Lin. 2017. Anserini: Enabling the use of lucene for information retrieval research. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’17, page 1253–1256, New York, NY, USA. Association for Compu… [find](https://scholar.google.com/scholar?q=Peilin+Yang%2C+Hui+Fang%2C+and+Jimmy+Lin%2E+2017%2E+Anserini%3A+Enabling+the+use+of+lucene+for+information+retrieval+research%2E+In+Proceedings+of+the+40th+International+ACM+SIGIR+Conference+on+Research+and+Devel)
39. Shi Yu, Jiahua Liu, Jingqin Yang, Chenyan Xiong, Paul Bennett, Jianfeng Gao, and Zhiyuan Liu. 2020. Few-shot generative conversational query rewriting. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’20, page 1933–1936, New Y… [find](https://scholar.google.com/scholar?q=Shi+Yu%2C+Jiahua+Liu%2C+Jingqin+Yang%2C+Chenyan+Xiong%2C+Paul+Bennett%2C+Jianfeng+Gao%2C+and+Zhiyuan+Liu%2E+2020%2E+Few%2Dshot+generative+conversational+query+rewriting%2E+In+Proceedings+of+the+43rd+International+ACM+SIG)
40. Shi Yu, Zhenghao Liu, Chenyan Xiong, Tao Feng, and Zhiyuan Liu. 2021. Few-Shot Conversational Dense Retrieval, page 829–838. Association for Computing Machinery, New York, NY, USA. [find](https://scholar.google.com/scholar?q=Shi+Yu%2C+Zhenghao+Liu%2C+Chenyan+Xiong%2C+Tao+Feng%2C+and+Zhiyuan+Liu%2E+2021%2E+Few%2DShot+Conversational+Dense+Retrieval%2C+page+829%E2%80%93838%2E+Association+for+Computing+Machinery%2C+New+York%2C+NY%2C+USA%2E)

*Updated 24 October 2026*