ABSTRACT
Recent research on conversational search highlights the importance of mixed-initiative in conversations. To enable mixed-initiative, the system should be able to ask clarifying questions to the user. However, the ability of the underlying ranking models (which support conversational search) to account for these clarifying questions and answers has not been analysed when ranking documents, at large. To this end, we analyse the performance of a lexical ranking model on a conversational search dataset with clarifying questions. We investigate, both quantitatively and qualitatively, how different aspects of clarifying questions and user answers affect the quality of ranking. We argue that there needs to be some fine-grained treatment of the entire conversational round of clarification, based on the explicit feedback which is present in such mixed-initiative settings. Informed by our findings, we introduce a simple heuristic-based lexical baseline, that significantly outperforms the existing naive baselines. Our work aims to enhance our understanding of the challenges present in this particular task and inform the design of more appropriate conversational ranking models.
Supplemental Material
- Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, and W Bruce Croft. 2019. Asking clarifying questions in open-domain information-seeking conversations. In SIGIR.Google Scholar
- Pavel Braslavski, Denis Savenkov, Eugene Agichtein, and Alina Dubatovka. 2017. What Do You Mean Exactly? Analyzing Clarification Questions in CQA. In CHIIR.Google Scholar
- Pawel Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, . 2018. MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling. In EMNLP.Google Scholar
- Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. 2018. QuAC: Question Answering in Context. In EMNLP.Google Scholar
- Helia Hashemi, Hamed Zamani, and W Bruce Croft. 2020. Guided Transformer:Leveraging Multiple External Sources for Representation Learning in Conversational Search. In SIGIR.Google Scholar
- Johannes Kiesel, Arefeh Bahrami, Benno Stein, Avishek Anand, and Matthias Hagen. 2018. Toward Voice Query Clarification. In SIGIR.Google Scholar
- Jay M Ponte and W Bruce Croft. 1998. A language modeling approach to information retrieval. In SIGIR.Google Scholar
- Filip Radlinski and Nick Craswell. 2017. A theoretical framework for conversational search. In ICTIR.Google Scholar
- Hamed Zamani, Bhaskar Mitra, Everest Chen, Gord Lueck, Fernando Diaz, Paul N Bennett, Nick Craswell, and Susan T Dumais. 2020. Analyzing and Learning from User Interactions for Search Clarification. In SIGIR.Google Scholar
Index Terms
- Analysing the Effect of Clarifying Questions on Document Ranking in Conversational Search
Recommendations
Asking Clarifying Questions Based on Negative Feedback in Conversational Search
ICTIR '21: Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information RetrievalUsers often need to look through multiple search result pages or reformulate queries when they have complex information-seeking needs. Conversational search systems make it possible to improve user satisfaction by asking questions to clarify users' ...
First International Workshop on Conversational Approaches to Information Retrieval (CAIR'17)
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information RetrievalRecent advances in commercial conversational services that allow naturally spoken and typed interaction, particularly for well-formulated questions and commands, have increased the need for more human-centric interactions in information retrieval. The ...
Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Search
WWW '24: Proceedings of the ACM on Web Conference 2024In mixed-initiative conversational search systems, clarifying questions aid users who struggle to express their intentions in a single query. These questions aim to uncover user's information needs and resolve query ambiguities. We hypothesize that in ...
Comments