ABSTRACT
Users' queries are usually vague, and their search intents tend to be ambiguous, thereby needing search clarification to clarify users' current intent by asking a clarifying question and providing several clickable sub-intent items as clarification options. However, in addition to drilling down the current query, users may also have exploratory needs that diverge from their current intent. For example, a user searching for the query "Cartier women watches'' may also potentially want to explore some parallel information by issuing queries such as "Rolex women watches'' or "Cartier women bracelets'', named exploratory queries in this paper. These exploratory needs are common during the search process yet cannot be satisfied by current search clarification approaches which typically stick to the sub-intents of the query. This paper focuses on mining exploratory queries as additional options to meet users' exploratory needs in conversational search systems. Specifically, we first design a rule-based model that generates exploratory queries based on the current query's top retrieved documents. Then, we propose using the data generated by the rule-based model to train a neural generation model through multi-task learning for further generalization. Finally, we borrow the in-context learning ability of the large language model to generate exploratory queries based on prompt engineering. We constructed an evaluation dataset based on human annotations and conduct an extensive set of experiments. The results show that our proposed methods generate higher-quality exploratory queries compared with several baselines.
Supplemental Material
- Wasi Uddin Ahmad and Kai-Wei Chang. 2018. Multi-task learning for document ranking and query suggestion. In Sixth International Conference on Learning Representations.Google Scholar
- Alfred V Aho and Margaret J Corasick. 1975. Efficient string matching: an aid to bibliographic search. Commun. ACM, Vol. 18, 6 (1975), 333--340.Google ScholarDigital Library
- Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, and W Bruce Croft. 2019. Asking clarifying questions in open-domain information-seeking conversations. In Proceedings of the 42nd international acm sigir conference on research and development in information retrieval. 475--484.Google ScholarDigital Library
- Paolo Boldi, Francesco Bonchi, Carlos Castillo, and Sebastiano Vigna. 2009. From" dango" to" japanese cakes": Query reformulation models and patterns. In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Vol. 1. IEEE, 183--190.Google ScholarDigital Library
- Fei Cai, Maarten De Rijke, et al. 2016. A survey of query auto completion in information retrieval. Foundations and Trends® in Information Retrieval, Vol. 10, 4 (2016), 273--363.Google Scholar
- Ruey-Cheng Chen and Chia-Jung Lee. 2020. Incorporating behavioral hypotheses for query generation. arXiv preprint arXiv:2010.02667 (2020).Google Scholar
- Mostafa Dehghani, Sascha Rothe, Enrique Alfonseca, and Pascal Fleury. 2017. Learning to attend, copy, and generate for session-based query suggestion. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 1747--1756.Google ScholarDigital Library
- Zhicheng Dou, Sha Hu, Yulong Luo, Ruihua Song, and Ji-Rong Wen. 2011. Finding dimensions for queries. In Proceedings of the 20th ACM international conference on Information and knowledge management. 1311--1320.Google ScholarDigital Library
- Zhicheng Dou, Zhengbao Jiang, Sha Hu, Ji-Rong Wen, and Ruihua Song. 2015. Automatically mining facets for queries from their search results. IEEE Transactions on knowledge and data engineering, Vol. 28, 2 (2015), 385--397.Google ScholarDigital Library
- Jiafeng Guo, Xueqi Cheng, Gu Xu, and Huawei Shen. 2010. A structured approach to query recommendation with social annotation data. In Proceedings of the 19th ACM international conference on Information and knowledge management. 619--628.Google ScholarDigital Library
- Helia Hashemi, Hamed Zamani, and W Bruce Croft. 2021. Learning multiple intent representations for search queries. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 669--679.Google ScholarDigital Library
- Helia Hashemi, Hamed Zamani, and W Bruce Croft. 2022. Stochastic Optimization of Text Set Generation for Learning Multiple Query Intent Representations. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 4003--4008.Google ScholarDigital Library
- Jyun-Yu Jiang and Wei Wang. 2018. RIN: Reformulation inference network for context-aware query suggestion. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 197--206.Google ScholarDigital Library
- Zhengbao Jiang, Zhicheng Dou, and Ji-Rong Wen. 2016. Generating query facets using knowledge bases. IEEE transactions on knowledge and data engineering, Vol. 29, 2 (2016), 315--329.Google Scholar
- Makoto P Kato, Tetsuya Sakai, and Katsumi Tanaka. 2012. Structured query suggestion for specialization and parallel movement: effect on search behaviors. In Proceedings of the 21st international conference on World Wide Web. 389--398.Google ScholarDigital Library
- Weize Kong and James Allan. 2013. Extracting query facets from search results. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. 93--102.Google ScholarDigital Library
- Weize Kong and James Allan. 2014. Extending faceted search to the general web. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. 839--848.Google ScholarDigital Library
- Alex Ksikes. 2014. Towards exploratory faceted search systems. Ph.,D. Dissertation. University of Cambridge.Google Scholar
- Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019).Google Scholar
- Ruirui Li, Liangda Li, Xian Wu, Yunhong Zhou, and Wei Wang. 2019. Click feedback-aware query recommendation using adversarial examples. In The World Wide Web Conference. 2978--2984.Google ScholarDigital Library
- Matteo Lissandrini, Davide Mottin, Themis Palpanas, and Yannis Velegrakis. 2020. Graph-query suggestions for knowledge graph exploration. In Proceedings of The Web Conference 2020. 2549--2555.Google ScholarDigital Library
- Chao Liu, Xuanlin Bao, Hongyu Zhang, Neng Zhang, Haibo Hu, Xiaohong Zhang, and Meng Yan. 2023 a. Improving ChatGPT Prompt for Code Generation. arxiv: 2305.08360 [cs.SE]Google Scholar
- Jiqun Liu. 2022. Toward Cranfield-Inspired Reusability Assessment in Interactive Information Retrieval Evaluation. Inf. Process. Manage. , Vol. 59, 5 (sep 2022), bibinfonumpages16 pages. https://doi.org/10.1016/j.ipm.2022.103007Google ScholarDigital Library
- Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 b. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. Comput. Surveys, Vol. 55, 9 (2023), 1--35.Google ScholarDigital Library
- Chao Ma and Bin Zhang. 2018. A new query recommendation method supporting exploratory search based on search goal shift graphs. IEEE Transactions on Knowledge and Data Engineering, Vol. 30, 11 (2018), 2024--2036.Google ScholarDigital Library
- Christopher D Manning, Mihai Surdeanu, John Bauer, Jenny Rose Finkel, Steven Bethard, and David McClosky. 2014. The Stanford CoreNLP natural language processing toolkit. In Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations. 55--60.Google ScholarCross Ref
- Agnès Mustar, Sylvain Lamprier, and Benjamin Piwowarski. 2020. Using BERT and BART for query suggestion. In Joint Conference of the Information Retrieval Communities in Europe, Vol. 2621. CEUR-WS. org.Google Scholar
- Filip Radlinski and Nick Craswell. 2017. A theoretical framework for conversational search. In Proceedings of the 2017 conference on conference human information interaction and retrieval. 117--126.Google ScholarDigital Library
- Chris Samarinas, Arkin Dharawat, and Hamed Zamani. 2022. Revisiting Open Domain Query Facet Extraction and Generation. In Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval. 43--50.Google ScholarDigital Library
- Ivan Sekulić, Mohammad Aliannejadi, and Fabio Crestani. 2021. Towards facet-driven generation of clarifying questions for conversational search. In Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval. 167--175.Google ScholarDigital Library
- Alessandro Sordoni, Yoshua Bengio, Hossein Vahabi, Christina Lioma, Jakob Grue Simonsen, and Jian-Yun Nie. 2015. A hierarchical recurrent encoder-decoder for generative context-aware query suggestion. In proceedings of the 24th ACM international on conference on information and knowledge management. 553--562.Google ScholarDigital Library
- Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. Advances in neural information processing systems , Vol. 27 (2014).Google Scholar
- Alexandra Vtyurina, Denis Savenkov, Eugene Agichtein, et al. 2017. Exploring conversational search with humans, assistants, and wizards. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. 2187--2193.Google ScholarDigital Library
- Zhongyuan Wang, Haixun Wang, Ji-Rong Wen, and Yanghua Xiao. 2015. An inference approach to basic level of categorization. In Proceedings of the 24th acm international on conference on information and knowledge management. 653--662.Google ScholarDigital Library
- Ryen W White and Resa A Roth. 2009. Exploratory search: Beyond the query-response paradigm. Synthesis lectures on information concepts, retrieval, and services, Vol. 1, 1 (2009), 1--98.Google ScholarDigital Library
- Wentao Wu, Hongsong Li, Haixun Wang, and Kenny Q Zhu. 2012. Probase: A probabilistic taxonomy for text understanding. In Proceedings of the 2012 ACM SIGMOD international conference on management of data. 481--492.Google ScholarDigital Library
- Hamed Zamani, Susan Dumais, Nick Craswell, Paul Bennett, and Gord Lueck. 2020a. Generating clarifying questions for information retrieval. In Proceedings of the web conference 2020. 418--428.Google ScholarDigital Library
- Hamed Zamani, Gord Lueck, Everest Chen, et al. 2020b. Mimics: A large-scale data collection for search clarification. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 3189--3196.Google ScholarDigital Library
- Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. arXiv preprint arXiv:1904.09675 (2019).Google Scholar
- Ziliang Zhao, Zhicheng Dou, Yu Guo, Zhao Cao, and Xiaohua Cheng. 2023. Improving Search Clarification with Structured Information Extracted from Search Results. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Long Beach, CA, USA) (KDD '23). Association for Computing Machinery, New York, NY, USA, 3549--3558. https://doi.org/10.1145/3580305.3599389Google ScholarDigital Library
- Ziliang Zhao, Zhicheng Dou, Jiaxin Mao, and Ji-Rong Wen. 2022. Generating Clarifying Questions with Web Search Results. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 234--244.Google ScholarDigital Library
- Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Zhicheng Dou, and Ji-Rong Wen. 2023. Large Language Models for Information Retrieval: A Survey. CoRR , Vol. abs/2308.07107 (2023). ioGoogle Scholar
Index Terms
- Mining Exploratory Queries for Conversational Search
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