A Survey of Conversational Search

J. Trienes and K. Balog, “Identifying unclear questions in community question answering websites,” CoRR , vol. abs/1901.06168, 2019. [Online]. Available: http://arxiv.org/abs/1901.06168

Original

1901

Earlier work this paper cites.

——, “Answer-based adversarial training for generating clarification questions,” CoRR , vol. abs/1904.02281, 2019. [Online]. Available: http://arxiv.org/abs/1904.02281

Original

1904

Earlier work this paper cites.

M. Aliannejadi, H. Zamani, F. Crestani, and W. B. Croft, “Asking clarifying questions in open-domain information-seeking conversations,” CoRR , vol. abs/1907.06554, 2019. [Online]. Available: http://arxiv.org/abs/1907.06554

Original

1907

Earlier work this paper cites.

Q. Chen, J. Lin, Y. Zhang, M. Ding, Y. Cen, H. Yang, and J. Tang, “Towards knowledge-based recommender dialog system,” CoRR , vol. abs/1908.05391, 2019. [Online]. Available: http://arxiv.org/abs/1908.05391

Original

1908

Earlier work this paper cites.

C. Qu, L. Yang, M. Qiu, Y. Zhang, C. Chen, W. B. Croft, and M. Iyyer, “Attentive history selection for conversational question answering,” CoRR , vol. abs/1908.09456, 2019. [Online]. Available: http://arxiv.org/abs/1908.09456

Original

1908

Earlier work this paper cites.

C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” CoRR , vol. abs/1910.10683, 2019. [Online]. Available: http://arxiv.org/abs/1910.10683

Original

1910

Earlier work this paper cites.

G. Penha, A. Balan, and C. Hauff, “Introducing mantis: a novel multi-domain information seeking dialogues dataset,” CoRR , vol. abs/1912.04639, 2019. [Online]. Available: http://arxiv.org/abs/1912.04639

Original

1912

Earlier work this paper cites.

J. Preece, Y. Rogers, H. Sharp, D. Benyon, S. Holland, and T. Carey, Human-computer interaction . Addison-Wesley Longman Ltd., 1994

1994

Earlier work this paper cites.

K. S. Jones, S. Walker, and S. E. Robertson, “A probabilistic model of information retrieval: development and comparative experiments - part 1,” Inf. Process. Manag. , vol. 36, no. 6, pp. 779–808, 2000. [Online]. Available: https://doi.org/10.1016/S0306-4573(00)00015-7

2000

Earlier work this paper cites.

C. Carpineto, R. de Mori, G. Romano, and B. Bigi, “An information-theoretic approach to automatic query expansion,” ACM Trans. Inf. Syst. , vol. 19, no. 1, pp. 1–27, 2001. [Online]. Available: https://doi.org/10.1145/366836.366860

2001

Earlier work this paper cites.

D. G. Bridge, “Towards conversational recommender systems: A dialogue grammar approach,” in 6th European Conference ov Case Based Reasoning, ECCBR 2002, Aberdeen, Scotland, UK, September 4-7, 2002, Workshop Proceedings , 2002, pp. 9–22

2002

Earlier work this paper cites.

W. Lei, X. He, Y. Miao, Q. Wu, R. Hong, M. Kan, and T. Chua, “Estimation-action-reflection: Towards deep interaction between conversational and recommender systems,” CoRR , vol. abs/2002.09102, 2020. [Online]. Available: https://arxiv.org/abs/2002.09102

Original

2002

Earlier work this paper cites.

J. Dalton, C. Xiong, and J. Callan, “TREC cast 2019: The conversational assistance track overview,” CoRR , vol. abs/2003.13624, 2020. [Online]. Available: https://arxiv.org/abs/2003.13624

Original

2003

Earlier work this paper cites.

——, “TREC cast 2019: The conversational assistance track overview,” CoRR , vol. abs/2003.13624, 2020. [Online]. Available: https://arxiv.org/abs/2003.13624

Original

2003

Earlier work this paper cites.

I. Mele, C. I. Muntean, F. M. Nardini, R. Perego, N. Tonellotto, and O. Frieder, “Topic propagation in conversational search,” CoRR , vol. abs/2004.14054, 2020. [Online]. Available: https://arxiv.org/abs/2004.14054

Original

2004

Earlier work this paper cites.

S. Lin, J. Yang, R. F. Nogueira, M. Tsai, C. Wang, and J. Lin, “Conversational question reformulation via sequence-to-sequence architectures and pretrained language models,” CoRR , vol. abs/2004.01909, 2020. [Online]. Available: https://arxiv.org/abs/2004.01909

Original

2004

Earlier work this paper cites.

S. Vakulenko, S. Longpre, Z. Tu, and R. Anantha, “Question rewriting for conversational question answering,” CoRR , vol. abs/2004.14652, 2020. [Online]. Available: https://arxiv.org/abs/2004.14652

Original

2004

Earlier work this paper cites.

D. Jannach, A. Manzoor, W. Cai, and L. Chen, “A survey on conversational recommender systems,” CoRR , vol. abs/2004.00646, 2020. [Online]. Available: https://arxiv.org/abs/2004.00646

Original

2004

Earlier work this paper cites.

H. Le, D. Sahoo, C. Liu, N. F. Chen, and S. C. H. Hoi, “Uniconv: A unified conversational neural architecture for multi-domain task-oriented dialogues,” CoRR , vol. abs/2004.14307, 2020. [Online]. Available: https://arxiv.org/abs/2004.14307

Original

2004

Earlier work this paper cites.

C. Qu, L. Yang, C. Chen, M. Qiu, W. B. Croft, and M. Iyyer, “Open-retrieval conversational question answering,” CoRR , vol. abs/2005.11364, 2020. [Online]. Available: https://arxiv.org/abs/2005.11364

Original

2005

Earlier work this paper cites.

N. Voskarides, D. Li, P. Ren, E. Kanoulas, and M. de Rijke, “Query resolution for conversational search with limited supervision,” CoRR , vol. abs/2005.11723, 2020. [Online]. Available: https://arxiv.org/abs/2005.11723

Original

2005

Earlier work this paper cites.

C. Qu, L. Yang, C. Chen, M. Qiu, W. B. Croft, and M. Iyyer, “Open-retrieval conversational question answering,” CoRR , vol. abs/2005.11364, 2020. [Online]. Available: https://arxiv.org/abs/2005.11364

Original

2005

Earlier work this paper cites.

S. Yu, J. Liu, J. Yang, C. Xiong, P. N. Bennett, J. Gao, and Z. Liu, “Few-shot generative conversational query rewriting,” CoRR , vol. abs/2006.05009, 2020. [Online]. Available: https://arxiv.org/abs/2006.05009

Original

2006

Earlier work this paper cites.

S. Yu, J. Liu, J. Yang, C. Xiong, P. N. Bennett, J. Gao, and Z. Liu, “Few-shot generative conversational query rewriting,” CoRR , vol. abs/2006.05009, 2020. [Online]. Available: https://arxiv.org/abs/2006.05009

Original

2006

Earlier work this paper cites.

H. Hashemi, H. Zamani, and W. B. Croft, “Guided transformer: Leveraging multiple external sources for representation learning in conversational search,” CoRR , vol. abs/2006.07548, 2020. [Online]. Available: https://arxiv.org/abs/2006.07548

Original

2006

Earlier work this paper cites.

H. Zamani, B. Mitra, E. Chen, G. Lueck, F. Diaz, P. N. Bennett, N. Craswell, and S. T. Dumais, “Analyzing and learning from user interactions for search clarification,” CoRR , vol. abs/2006.00166, 2020. [Online]. Available: https://arxiv.org/abs/2006.00166

Original

2006

Earlier work this paper cites.

H. Zamani, G. Lueck, E. Chen, R. Quispe, F. Luu, and N. Craswell, “MIMICS: A large-scale data collection for search clarification,” CoRR , vol. abs/2006.10174, 2020. [Online]. Available: https://arxiv.org/abs/2006.10174

Original

2006

Earlier work this paper cites.

V. Kumar and A. W. Black, “Clarq: A large-scale and diverse dataset for clarification question generation,” CoRR , vol. abs/2006.05986, 2020. [Online]. Available: https://arxiv.org/abs/2006.05986

Original

2006

Earlier work this paper cites.

K. Zhou, W. X. Zhao, S. Bian, Y. Zhou, J. Wen, and J. Yu, “Improving conversational recommender systems via knowledge graph based semantic fusion,” CoRR , vol. abs/2007.04032, 2020. [Online]. Available: https://arxiv.org/abs/2007.04032

Original

2007

Earlier work this paper cites.

W. Lei, G. Zhang, X. He, Y. Miao, X. Wang, L. Chen, and T. Chua, “Interactive path reasoning on graph for conversational recommendation,” CoRR , vol. abs/2007.00194, 2020. [Online]. Available: https://arxiv.org/abs/2007.00194

Original

2007

Earlier work this paper cites.

T. Talvensaari, M. Juhola, J. Laurikkala, and K. Järvelin, “Corpus-based cross-language information retrieval in retrieval of highly relevant documents,” J. Assoc. Inf. Sci. Technol. , vol. 58, no. 3, pp. 322–334, 2007. [Online]. Available: https://doi.org/10.1002/asi.20495

2007

Earlier work this paper cites.

V. Kumar, V. Raunak, and J. Callan, “Ranking clarification questions via natural language inference,” CoRR , vol. abs/2008.07688, 2020. [Online]. Available: https://arxiv.org/abs/2008.07688

Original

2008

Earlier work this paper cites.

G. Cao, J. Nie, J. Gao, and S. Robertson, “Selecting good expansion terms for pseudo-relevance feedback,” in Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, Singapore, July 20-24, 2008 , S. Myaeng, D. W. Oard, F. Sebastiani, T. Chua, and M. Leong, Eds. ACM, 2008, pp. 243–250. [Online]. Available: https://doi.org/10.1145/1390334.1390377

2008

Earlier work this paper cites.

A. M. Krasakis, M. Aliannejadi, N. Voskarides, and E. Kanoulas, “Analysing the effect of clarifying questions on document ranking in conversational search,” CoRR , vol. abs/2008.03717, 2020. [Online]. Available: https://arxiv.org/abs/2008.03717

Original

2008

Earlier work this paper cites.

M. Aliannejadi, J. Kiseleva, A. Chuklin, J. Dalton, and M. Burtsev, “Convai3: Generating clarifying questions for open-domain dialogue systems (clariq),” CoRR , vol. abs/2009.11352, 2020. [Online]. Available: https://arxiv.org/abs/2009.11352

Original

2009

Earlier work this paper cites.

R. Anantha, S. Vakulenko, Z. Tu, S. Longpre, S. Pulman, and S. Chappidi, “Open-domain question answering goes conversational via question rewriting,” CoRR , vol. abs/2010.04898, 2020. [Online]. Available: https://arxiv.org/abs/2010.04898

Original

2010

Earlier work this paper cites.

K. Zhou, Y. Zhou, W. X. Zhao, X. Wang, and J. Wen, “Towards topic-guided conversational recommender system,” CoRR , vol. abs/2010.04125, 2020. [Online]. Available: https://arxiv.org/abs/2010.04125

Original

2010

Earlier work this paper cites.

Y. Qu, Y. Ding, J. Liu, K. Liu, R. Ren, X. Zhao, D. Dong, H. Wu, and H. Wang, “Rocketqa: An optimized training approach to dense passage retrieval for open-domain question answering,” CoRR , vol. abs/2010.08191, 2020. [Online]. Available: https://arxiv.org/abs/2010.08191

Original

2010

Earlier work this paper cites.

J. Robinson, C. Chuang, S. Sra, and S. Jegelka, “Contrastive learning with hard negative samples,” CoRR , vol. abs/2010.04592, 2020. [Online]. Available: https://arxiv.org/abs/2010.04592

Original

2010

Earlier work this paper cites.

Z. Dou, S. Hu, Y. Luo, R. Song, and J. Wen, “Finding dimensions for queries,” in Proceedings of the 20th ACM Conference on Information and Knowledge Management, CIKM 2011, Glasgow, United Kingdom, October 24-28, 2011 , C. Macdonald, I. Ounis, and I. Ruthven, Eds. ACM, 2011, pp. 1311–1320. [Online]. Available: https://doi.org/10.1145/2063576.2063767

2011

Earlier work this paper cites.

W. Tannebaum and A. Rauber, “Acquiring lexical knowledge from query logs for query expansion in patent searching,” in Sixth IEEE International Conference on Semantic Computing, ICSC 2012, Palermo, Italy, September 19-21, 2012 . IEEE Computer Society, 2012, pp. 336–338. [Online]. Available: https://doi.org/10.1109/ICSC.2012.15

2012

Earlier work this paper cites.

C. L. A. Clarke, N. Craswell, and E. M. Voorhees, “Overview of the TREC 2012 web track,” in Proceedings of The Twenty-First Text REtrieval Conference, TREC 2012, Gaithersburg, Maryland, USA, November 6-9, 2012 , ser. NIST Special Publication, E. M. Voorhees and L. P. Buckland, Eds., vol. 500-298. National Institute of Standards and Technology (NIST), 2012. [Online]. Available: http://trec.nist.gov/pubs/trec21/papers/WEB12.overview.pdf

2012

Earlier work this paper cites.

W. Kong and J. Allan, “Extracting query facets from search results,” in The 36th International ACM SIGIR conference on research and development in Information Retrieval, SIGIR ’13, Dublin, Ireland - July 28 - August 01, 2013 , G. J. F. Jones, P. Sheridan, D. Kelly, M. de Rijke, and T. Sakai, Eds. ACM, 2013, pp. 93–102. [Online]. Available: https://doi.org/10.1145/2484028.2484097

2013

Earlier work this paper cites.

——, “Extending faceted search to the general web,” in Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, November 3-7, 2014 , J. Li, X. S. Wang, M. N. Garofalakis, I. Soboroff, T. Suel, and M. Wang, Eds. ACM, 2014, pp. 839–848. [Online]. Available: https://doi.org/10.1145/2661829.2661964

2014

Earlier work this paper cites.

B. He and I. Ounis, “Finding good feedback documents,” in Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China, November 2-6, 2009 , D. W. Cheung, I. Song, W. W. Chu, X. Hu, and J. Lin, Eds. ACM, 2009, pp. 2011–2014. [Online]. Available: https://doi.org/10.1145/1645953.1646289

2014

Earlier work this paper cites.

B. G. Slocum, “Conversational implicatures and legal texts,” Ratio juris: An international journal of jurisprudence and philosophy of law , vol. 29, no. 1, pp. 23–43, 2016

2016

Earlier work this paper cites.

R. F. Nogueira and K. Cho, “Task-oriented query reformulation with reinforcement learning,” CoRR , vol. abs/1704.04572, 2017. [Online]. Available: http://arxiv.org/abs/1704.04572

Original

2017

Earlier work this paper cites.

P. Braslavski, D. Savenkov, E. Agichtein, and A. Dubatovka, “What do you mean exactly?: Analyzing clarification questions in CQA,” in Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval, CHIIR 2017, Oslo, Norway, March 7-11, 2017 , R. Nordlie, N. Pharo, L. Freund, B. Larsen, and D. Russel, Eds. ACM, 2017, pp. 345–348. [Online]. Available: https://doi.org/10.1145/3020165.3022149

2017

Earlier work this paper cites.

F. Radlinski and N. Craswell, “A theoretical framework for conversational search,” in Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval, CHIIR 2017, Oslo, Norway, March 7-11, 2017 , R. Nordlie, N. Pharo, L. Freund, B. Larsen, and D. Russel, Eds. ACM, 2017, pp. 117–126. [Online]. Available: https://doi.org/10.1145/3020165.3020183

2017

Earlier work this paper cites.

J. R. Trippas, D. Spina, L. Cavedon, and M. Sanderson, “How do people interact in conversational speech-only search tasks: A preliminary analysis,” in Proceedings of the 2017 conference on conference human information interaction and retrieval , 2017, pp. 325–328

2017

Earlier work this paper cites.

J. S. Culpepper, F. Diaz, and M. D. Smucker, “Research frontiers in information retrieval: Report from the third strategic workshop on information retrieval in lorne (SWIRL 2018),” SIGIR Forum , vol. 52, no. 1, pp. 34–90, 2018. [Online]. Available: https://doi.org/10.1145/3274784.3274788

2018

Earlier work this paper cites.

S. Rao and H. D. III, “Learning to ask good questions: Ranking clarification questions using neural expected value of perfect information,” CoRR , vol. abs/1805.04655, 2018. [Online]. Available: http://arxiv.org/abs/1805.04655

Original

2018

Earlier work this paper cites.

R. Li, S. E. Kahou, H. Schulz, V. Michalski, L. Charlin, and C. Pal, “Towards deep conversational recommendations,” CoRR , vol. abs/1812.07617, 2018. [Online]. Available: http://arxiv.org/abs/1812.07617

Original

2018

Earlier work this paper cites.

C. Qu, L. Yang, W. B. Croft, J. R. Trippas, Y. Zhang, and M. Qiu, “Analyzing and characterizing user intent in information-seeking conversations,” CoRR , vol. abs/1804.08759, 2018. [Online]. Available: http://arxiv.org/abs/1804.08759

Original

2018

Earlier work this paper cites.

E. Dinan, S. Roller, K. Shuster, A. Fan, M. Auli, and J. Weston, “Wizard of wikipedia: Knowledge-powered conversational agents,” CoRR , vol. abs/1811.01241, 2018. [Online]. Available: http://arxiv.org/abs/1811.01241

Original

2018

Earlier work this paper cites.

J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” CoRR , vol. abs/1810.04805, 2018. [Online]. Available: http://arxiv.org/abs/1810.04805

Original

2018

Earlier work this paper cites.

A. Elgohary, D. Peskov, and J. L. Boyd-Graber, “Can you unpack that? learning to rewrite questions-in-context,” 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 2019, Hong Kong, China, November 3-7, 2019 , K. Inui, J. Jiang, V. Ng, and X. Wan, Eds. Association for Computational Linguistics, 2019, pp. 5917–5923. [Online]. Available: https://doi.org/10.18653/v1/D19-1605

2019

Earlier work this paper cites.

J. Xu, Y. Wang, D. Tang, N. Duan, P. Yang, Q. Zeng, M. Zhou, and X. Sun, “Asking clarification questions in knowledge-based question answering,” 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 2019, Hong Kong, China, November 3-7, 2019 , K. Inui, J. Jiang, V. Ng, and X. Wan, Eds. Association for Computational Linguistics, 2019, pp. 1618–1629. [Online]. Available: https://doi.org/10.18653/v1/D19-1172

2019

Earlier work this paper cites.

V. Kumar and J. Callan, “Making information seeking easier: An improved pipeline for conversational search,” in Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16-20 November 2020 , ser. Findings of ACL, T. Cohn, Y. He, and Y. Liu, Eds., vol. EMNLP 2020. Association for Computational Linguistics, 2020, pp. 3971–3980. [Online]. Available: https://doi.org/10.18653/v1/2020.findings-emnlp.354

2020

Earlier work this paper cites.

H. Zamani, S. T. Dumais, N. Craswell, P. N. Bennett, and G. Lueck, “Generating clarifying questions for information retrieval,” in WWW ’20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020 , Y. Huang, I. King, T. Liu, and M. van Steen, Eds. ACM / IW3C2, 2020, pp. 418–428. [Online]. Available: https://doi.org/10.1145/3366423.3380126

2020

Earlier work this paper cites.

L. Tavakoli, “Generating clarifying questions in conversational search systems,” in CIKM ’20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020 , M. d’Aquin, S. Dietze, C. Hauff, E. Curry, and P. Cudré-Mauroux, Eds. ACM, 2020, pp. 3253–3256. [Online]. Available: https://doi.org/10.1145/3340531.3418513

2020

Earlier work this paper cites.

W. Lei, X. He, M. de Rijke, and T. Chua, “Conversational recommendation: Formulation, methods, and evaluation,” in Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020 , J. X. Huang, Y. Chang, X. Cheng, J. Kamps, V. Murdock, J. Wen, and Y. Liu, Eds. ACM, 2020, pp. 2425–2428. [Online]. Available: https://doi.org/10.1145/3397271.3401419

2020

Earlier work this paper cites.

P. S. H. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, T. Rocktäschel, S. Riedel, and D. Kiela, “Retrieval-augmented generation for knowledge-intensive NLP tasks,” CoRR , vol. abs/2005.11401, 2020

Original

2020

Earlier work this paper cites.

P. Ren, Z. Liu, X. Song, H. Tian, Z. Chen, Z. Ren, and M. de Rijke, “Wizard of search engine: Access to information through conversations with search engines,” CoRR , vol. abs/2105.08301, 2021. [Online]. Available: https://arxiv.org/abs/2105.08301

Original

2021

Earlier work this paper cites.

C. Gao, W. Lei, X. He, M. de Rijke, and T. Chua, “Advances and challenges in conversational recommender systems: A survey,” AI Open , vol. 2, pp. 100–126, 2021. [Online]. Available: https://doi.org/10.1016/j.aiopen.2021.06.002

2021

Earlier work this paper cites.

R. Bhowmik, K. Stratos, and G. de Melo, “Fast and effective biomedical entity linking using a dual encoder,” CoRR , vol. abs/2103.05028, 2021. [Online]. Available: https://arxiv.org/abs/2103.05028

Original

2021

Earlier work this paper cites.

S. Sharma, J. Brennan, and J. R. C. Nurse, “Stockbabble: A conversational financial agent to support stock market investors,” CoRR , vol. abs/2106.08298, 2021. [Online]. Available: https://arxiv.org/abs/2106.08298

Original

2021

Earlier work this paper cites.

B. Liu, Y. Wu, Y. Liu, F. Zhang, Y. Shao, C. Li, M. Zhang, and S. Ma, “Conversational vs traditional: Comparing search behavior and outcome in legal case retrieval,” in SIGIR ’21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021 , F. Diaz, C. Shah, T. Suel, P. Castells, R. Jones, and T. Sakai, Eds. ACM, 2021, pp. 1622–1626. [Online]. Available: https://doi.org/10.1145/3404835.3463064

2021

Earlier work this paper cites.

S. Lin, J. Yang, and J. Lin, “Contextualized query embeddings for conversational search,” CoRR , vol. abs/2104.08707, 2021. [Online]. Available: https://arxiv.org/abs/2104.08707

Original

2021

Earlier work this paper cites.

M. D. Tredici, G. Barlacchi, X. Shen, W. Cheng, and A. de Gispert, “Question rewriting for open-domain conversational QA: best practices and limitations,” in CIKM ’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021 , G. Demartini, G. Zuccon, J. S. Culpepper, Z. Huang, and H. Tong, Eds. ACM, 2021, pp. 2974–2978. [Online]. Available: https://doi.org/10.1145/3459637.3482164

2021

Earlier work this paper cites.

S. Vakulenko, N. Voskarides, Z. Tu, and S. Longpre, “A comparison of question rewriting methods for conversational passage retrieval,” CoRR , vol. abs/2101.07382, 2021. [Online]. Available: https://arxiv.org/abs/2101.07382

Original

2021

Earlier work this paper cites.

Z. Wu, Y. Luan, H. Rashkin, D. Reitter, and G. S. Tomar, “CONQRR: conversational query rewriting for retrieval with reinforcement learning,” CoRR , vol. abs/2112.08558, 2021. [Online]. Available: https://arxiv.org/abs/2112.08558

Original

2021

Earlier work this paper cites.

——, “TREC cast 2021: The conversational assistance track overview,” in Proceedings of the Thirtieth Text REtrieval Conference, TREC 2021, online, November 15-19, 2021 , ser. NIST Special Publication, I. Soboroff and A. Ellis, Eds., vol. 500-335. National Institute of Standards and Technology (NIST), 2021. [Online]. Available: https://trec.nist.gov/pubs/trec30/papers/Overview-CAsT.pdf

2021

Earlier work this paper cites.

K. Bi, Q. Ai, and W. B. Croft, “Asking clarifying questions based on negative feedback in conversational search,” CoRR , vol. abs/2107.05760, 2021. [Online]. Available: https://arxiv.org/abs/2107.05760

Original

2021

Earlier work this paper cites.

Y. Mass, D. Cohen, A. Yehudai, and D. Konopnicki, “Conversational search with mixed-initiative - asking good clarification questions backed-up by passage retrieval,” CoRR , vol. abs/2112.07308, 2021. [Online]. Available: https://arxiv.org/abs/2112.07308

Original

2021

Earlier work this paper cites.

I. Sekulic, M. Aliannejadi, and F. Crestani, “Towards facet-driven generation of clarifying questions for conversational search,” in ICTIR ’21: The 2021 ACM SIGIR International Conference on the Theory of Information Retrieval, Virtual Event, Canada, July 11, 2021 , F. Hasibi, Y. Fang, and A. Aizawa, Eds. ACM, 2021, pp. 167–175. [Online]. Available: https://doi.org/10.1145/3471158.3472257

2021

Earlier work this paper cites.

J. Wang and W. Li, “Template-guided clarifying question generation for web search clarification,” in CIKM ’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021 , G. Demartini, G. Zuccon, J. S. Culpepper, Z. Huang, and H. Tong, Eds. ACM, 2021, pp. 3468–3472. [Online]. Available: https://doi.org/10.1145/3459637.3482199

2021

Earlier work this paper cites.

H. Hashemi, H. Zamani, and W. B. Croft, “Learning multiple intent representations for search queries,” in CIKM ’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021 , G. Demartini, G. Zuccon, J. S. Culpepper, Z. Huang, and H. Tong, Eds. ACM, 2021, pp. 669–679. [Online]. Available: https://doi.org/10.1145/3459637.3482445

2021

Earlier work this paper cites.

I. Sekulic, M. Aliannejadi, and F. Crestani, “User engagement prediction for clarification in search,” CoRR , vol. abs/2102.04163, 2021. [Online]. Available: https://arxiv.org/abs/2102.04163

Original

2021

Earlier work this paper cites.

T. Lotze, S. Klut, M. Aliannejadi, and E. Kanoulas, “Ranking clarifying questions based on predicted user engagement,” CoRR , vol. abs/2103.06192, 2021. [Online]. Available: https://arxiv.org/abs/2103.06192

Original

2021

Earlier work this paper cites.

Y. Deng, Y. Li, F. Sun, B. Ding, and W. Lam, “Unified conversational recommendation policy learning via graph-based reinforcement learning,” CoRR , vol. abs/2105.09710, 2021. [Online]. Available: https://arxiv.org/abs/2105.09710

Original

2021

Earlier work this paper cites.

J. White, G. Poesia, R. X. D. Hawkins, D. Sadigh, and N. D. Goodman, “Open-domain clarification question generation without question examples,” CoRR , vol. abs/2110.09779, 2021. [Online]. Available: https://arxiv.org/abs/2110.09779

Original

2021

Earlier work this paper cites.

B. P. Majumder, S. Rao, M. Galley, and J. J. McAuley, “Ask what’s missing and what’s useful: Improving clarification question generation using global knowledge,” CoRR , vol. abs/2104.06828, 2021. [Online]. Available: https://arxiv.org/abs/2104.06828

Original

2021

Earlier work this paper cites.

Z. Zhang and K. Q. Zhu, “Diverse and specific clarification question generation with keywords,” CoRR , vol. abs/2104.10317, 2021. [Online]. Available: https://arxiv.org/abs/2104.10317

Original

2021

Earlier work this paper cites.

Y. Li, W. Li, and L. Nie, “A graph-guided multi-round retrieval method for conversational open-domain question answering,” CoRR , vol. abs/2104.08443, 2021. [Online]. Available: https://arxiv.org/abs/2104.08443

Original

2021

Earlier work this paper cites.

S. Yu, Z. Liu, C. Xiong, T. Feng, and Z. Liu, “Few-shot conversational dense retrieval,” CoRR , vol. abs/2105.04166, 2021. [Online]. Available: https://arxiv.org/abs/2105.04166

Original

2021

Earlier work this paper cites.

S. Lin, J. Yang, and J. Lin, “Contextualized query embeddings for conversational search,” CoRR , vol. abs/2104.08707, 2021. [Online]. Available: https://arxiv.org/abs/2104.08707

Original

2021

Earlier work this paper cites.

R. Nakano, J. Hilton, S. Balaji, J. Wu, L. Ouyang, C. Kim, C. Hesse, S. Jain, V. Kosaraju, W. Saunders, X. Jiang, K. Cobbe, T. Eloundou, G. Krueger, K. Button, M. Knight, B. Chess, and J. Schulman, “Webgpt: Browser-assisted question-answering with human feedback,” CoRR , vol. abs/2112.09332, 2021. [Online]. Available: https://arxiv.org/abs/2112.09332

Original

2021

Earlier work this paper cites.

L. Liao, L. H. Long, Z. Zhang, M. Huang, and T. Chua, “Mmconv: An environment for multimodal conversational search across multiple domains,” in SIGIR ’21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021 , F. Diaz, C. Shah, T. Suel, P. Castells, R. Jones, and T. Sakai, Eds. ACM, 2021, pp. 675–684. [Online]. Available: https://doi.org/10.1145/3404835.3462970

2021

Earlier work this paper cites.

H. Li, T. Gao, M. Goenka, and D. Chen, “Ditch the gold standard: Re-evaluating conversational question answering,” CoRR , vol. abs/2112.08812, 2021. [Online]. Available: https://arxiv.org/abs/2112.08812

Original

2021

Earlier work this paper cites.

W. Siblini, B. Sayil, and Y. Kessaci, “Towards a more robust evaluation for conversational question answering,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 2: Short Papers), Virtual Event, August 1-6, 2021 , C. Zong, F. Xia, W. Li, and R. Navigli, Eds. Association for Computational Linguistics, 2021, pp. 1028–1034. [Online]. Available: https://doi.org/10.18653/v1/2021.acl-short.130

2021

Earlier work this paper cites.

A. Salle, S. Malmasi, O. Rokhlenko, and E. Agichtein, “Studying the effectiveness of conversational search refinement through user simulation,” in Advances in Information Retrieval - 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28 - April 1, 2021, Proceedings, Part I , ser. Lecture Notes in Computer Science, D. Hiemstra, M. Moens, J. Mothe, R. Perego, M. Potthast, and F. Sebastiani, Eds., vol. 12656. Springer, 2021, pp. 587–602. [Online]. Available: https://doi.org/10.1007/978-3-030-72113-8_39

2021

Earlier work this paper cites.

A. Lipani, B. Carterette, and E. Yilmaz, “How am I doing?: Evaluating conversational search systems offline,” ACM Trans. Inf. Syst. , vol. 39, no. 4, pp. 51:1–51:22, 2021. [Online]. Available: https://doi.org/10.1145/3451160

2021

Earlier work this paper cites.

S. Feng, S. S. Patel, H. Wan, and S. Joshi, “Multidoc2dial: Modeling dialogues grounded in multiple documents,” CoRR , vol. abs/2109.12595, 2021. [Online]. Available: https://arxiv.org/abs/2109.12595

Original

2021

Earlier work this paper cites.

M. Guo, M. Zhang, S. Reddy, and M. Alikhani, “Abg-coqa: Clarifying ambiguity in conversational question answering,” in 3rd Conference on Automated Knowledge Base Construction, AKBC 2021, Virtual, October 4-8, 2021 , D. Chen, J. Berant, A. McCallum, and S. Singh, Eds., 2021. [Online]. Available: https://doi.org/10.24432/C5F30Z

2021

Earlier work this paper cites.

P. Ren, Z. Chen, Z. Ren, E. Kanoulas, C. Monz, and M. de Rijke, “Conversations with search engines: Serp-based conversational response generation,” ACM Trans. Inf. Syst. , vol. 39, no. 4, pp. 47:1–47:29, 2021. [Online]. Available: https://doi.org/10.1145/3432726

2021

Earlier work this paper cites.

S. Lin, J. Yang, R. F. Nogueira, M. Tsai, C. Wang, and J. Lin, “Multi-stage conversational passage retrieval: An approach to fusing term importance estimation and neural query rewriting,” ACM Trans. Inf. Syst. , vol. 39, no. 4, pp. 48:1–48:29, 2021. [Online]. Available: https://doi.org/10.1145/3446426

2021

Earlier work this paper cites.

M. Aliannejadi, J. Kiseleva, A. Chuklin, J. Dalton, and M. Burtsev, “Building and evaluating open-domain dialogue corpora with clarifying questions,” CoRR , vol. abs/2109.05794, 2021. [Online]. Available: https://arxiv.org/abs/2109.05794

Original

2021

Earlier work this paper cites.

M. Aliannejadi, L. Azzopardi, H. Zamani, E. Kanoulas, P. Thomas, and N. Craswell, “Analysing mixed initiatives and search strategies during conversational search,” CoRR , vol. abs/2109.05955, 2021. [Online]. Available: https://arxiv.org/abs/2109.05955

Original

2021

Earlier work this paper cites.

C. Gao, W. Lei, X. He, M. de Rijke, and T. Chua, “Advances and challenges in conversational recommender systems: A survey,” AI Open , vol. 2, pp. 100–126, 2021. [Online]. Available: https://doi.org/10.1016/j.aiopen.2021.06.002

2021

Earlier work this paper cites.

T. Formal, B. Piwowarski, and S. Clinchant, “SPLADE: sparse lexical and expansion model for first stage ranking,” CoRR , vol. abs/2107.05720, 2021. [Online]. Available: https://arxiv.org/abs/2107.05720

Original

2021

Earlier work this paper cites.

H. Jeon and G. G. Lee, “Domain state tracking for a simplified dialogue system,” CoRR , vol. abs/2103.06648, 2021. [Online]. Available: https://arxiv.org/abs/2103.06648

Original

2021

Cited alongside, same era.

Z. Liu, D. Huang, K. Huang, Z. Li, and J. Zhao, “Finbert: A pre-trained financial language representation model for financial text mining,” in Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence , 2021, pp. 4513–4519

2021

Cited alongside, same era.

K. Mao, Z. Dou, H. Qian, F. Mo, X. Cheng, and Z. Cao, “Convtrans: Transforming web search sessions for conversational dense retrieval,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022 , Y. Goldberg, Z. Kozareva, and Y. Zhang, Eds. Association for Computational Linguistics, 2022, pp. 2935–2946. [Online]. Available: https://doi.org/10.18653/v1/2022.emnlp-main.190

2022

Cited alongside, same era.

C. Meng, M. Aliannejadi, and M. de Rijke, “System initiative prediction for multi-turn conversational information seeking,” in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023 , I. Frommholz, F. Hopfgartner, M. Lee, M. Oakes, M. Lalmas, M. Zhang, and R. L. T. Santos, Eds. ACM, 2023, pp. 1807–1817. [Online]. Available: https://doi.org/10.1145/3583780.3615070

2023

Later among the works it cites.

X. Fu and A. Lipani, “Priming and actions: An analysis in conversational search systems,” in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023 , H. Chen, W. E. Duh, H. Huang, M. P. Kato, J. Mothe, and B. Poblete, Eds. ACM, 2023, pp. 2277–2281. [Online]. Available: https://doi.org/10.1145/3539618.3592041

2023

Later among the works it cites.

W. Lajewska and K. Balog, “Towards filling the gap in conversational search: From passage retrieval to conversational response generation,” CoRR , vol. abs/2308.08911, 2023. [Online]. Available: https://doi.org/10.48550/arXiv.2308.08911

Original