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Generating Clarifying Questions for Information Retrieval

Authors:

Gord LueckAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 418 - 428

https://doi.org/10.1145/3366423.3380126

Published: 20 April 2020 Publication History

Abstract

Search queries are often short, and the underlying user intent may be ambiguous. This makes it challenging for search engines to predict possible intents, only one of which may pertain to the current user. To address this issue, search engines often diversify the result list and present documents relevant to multiple intents of the query. An alternative approach is to ask the user a question to clarify her information need. Asking clarifying questions is particularly important for scenarios with “limited bandwidth” interfaces, such as speech-only and small-screen devices. In addition, our user studies and large-scale online experiments show that asking clarifying questions is also useful in web search. Although some recent studies have pointed out the importance of asking clarifying questions, generating them for open-domain search tasks remains unstudied and is the focus of this paper. Lack of training data even within major search engines for this task makes it challenging. To mitigate this issue, we first identify a taxonomy of clarification for open-domain search queries by analyzing large-scale query reformulation data sampled from Bing search logs. This taxonomy leads us to a set of question templates and a simple yet effective slot filling algorithm. We further use this model as a source of weak supervision to automatically generate clarifying questions for training. Furthermore, we propose supervised and reinforcement learning models for generating clarifying questions learned from weak supervision data. We also investigate methods for generating candidate answers for each clarifying question, so users can select from a set of pre-defined answers. Human evaluation of the clarifying questions and candidate answers for hundreds of search queries demonstrates the effectiveness of the proposed solutions.

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Cited By

Tavakoli LTrippas JZamani HScholer FSanderson M(2024)Online and Offline Evaluation in Search ClarificationACM Transactions on Information Systems10.1145/368178643:1(1-30)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3681786
Sekulić ILu LBedi NCrestani FSakai TIshita EOhshima HRadboud University, Netherlands FMao JJose J(2024)Simulating Conversational Search Users with Parameterized BehaviorProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698425(72-81)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698425
Yanagi RTogo ROgawa THaseyama MCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)DQG: Database Question Generation for Exact Text-based Image RetrievalProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681469(7424-7433)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681469
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Index Terms

Generating Clarifying Questions for Information Retrieval
1. Information systems
  1. Information retrieval

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

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Published: 20 April 2020

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Cited By

Tavakoli LTrippas JZamani HScholer FSanderson M(2024)Online and Offline Evaluation in Search ClarificationACM Transactions on Information Systems10.1145/368178643:1(1-30)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3681786
Sekulić ILu LBedi NCrestani FSakai TIshita EOhshima HRadboud University, Netherlands FMao JJose J(2024)Simulating Conversational Search Users with Parameterized BehaviorProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698425(72-81)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698425
Yanagi RTogo ROgawa THaseyama MCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)DQG: Database Question Generation for Exact Text-based Image RetrievalProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681469(7424-7433)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681469
White R(2024)Advancing the Search Frontier with AI AgentsCommunications of the ACM10.1145/3655615Online publication date: 20-Aug-2024
https://doi.org/10.1145/3655615
Sekulić IAlinannejadi MCrestani F(2024)Analysing Utterances in LLM-Based User Simulation for Conversational SearchACM Transactions on Intelligent Systems and Technology10.1145/365004115:3(1-22)Online publication date: 5-Mar-2024
https://dl.acm.org/doi/10.1145/3650041
Ye DLiu JFan JTian BZhou TChen XMa JBaeza-Yates RBonchi F(2024)Enhancing Asymmetric Web Search through Question-Answer Generation and RankingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671517(6127-6136)Online publication date: 25-Aug-2024
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Zhao ZDou ZZhou YSerra ESpezzano F(2024)Generating Intent-aware Clarifying Questions in Conversational Information Retrieval SystemsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679851(3384-3394)Online publication date: 21-Oct-2024
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Anand AV VSetty VAnand AHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)The Surprising Effectiveness of Rankers trained on Expanded QueriesProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657938(2652-2656)Online publication date: 10-Jul-2024
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Samarinas CZamani HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)ProCIS: A Benchmark for Proactive Retrieval in ConversationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657869(830-840)Online publication date: 10-Jul-2024
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Deng YLiao LZheng ZYang GChua THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Towards Human-centered Proactive Conversational AgentsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657843(807-818)Online publication date: 10-Jul-2024
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