short-paper

Toward Voice Query Clarification

Authors:

Matthias HagenAuthors Info & Claims

SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

Pages 1257 - 1260

https://doi.org/10.1145/3209978.3210160

Published: 27 June 2018 Publication History

Get Access

Abstract

Query suggestions are a standard means to clarify the intent of underspecified queries. In a voice-based search setting, the compilation of query suggestions is not straightforward, and user-centric research targeting query underspecification is lacking so far. Our paper analyses a specific type of ambiguous voice queries and studies the impact of various kinds of voice query clarifications offered by the system and its impact on user satisfaction. We conduct a user study that measures the satisfaction for clarifications that are explicitly invoked and presented by seven different methods. Our findings include that (1) user experience depends on language proficiency levels, (2) users are not dissatisfied when prompted for clarifications (in fact, enjoy it sometimes), and (3) the most effective way of query clarification depends on the number and lengths of the possible answers.

References

[1]

James Allan, W. Bruce Croft, Alistair Moffat, and Mark Sanderson . 2012. Frontiers, challenges, and opportunities for information retrieval: Report from SWIRL 2012. SIGIR Forum, Vol. 46, 1 (2012), 2--32.

Digital Library

Google Scholar

[2]

Sumit Bhatia, Debapriyo Majumdar, and Prasenjit Mitra . 2011. Query suggestions in the absence of query logs. In SIGIR. ACM, 795--804.

Digital Library

Google Scholar

[3]

Pavel Braslavski, Denis Savenkov, Eugene Agichtein, and Alina Dubatovka . 2017. What Do You Mean Exactly?. In CHIIR. ACM, 345--348.

Digital Library

Google Scholar

[4]

Jennifer Lai and Nicole Yankelovich . 2006. Speech Interface Design. Encyclopedia of Language & Linguistics, bibfieldeditorKeith Brown (Ed.). Elsevier, Oxford, 764--770.

Google Scholar

[5]

Ewa Luger and Abigail Sellen . 2016. "Like Having a Really Bad PA": The Gulf between User Expectation and Experience of Conversational Agents. In CHI. ACM, 5286--5297.

Digital Library

Google Scholar

[6]

Filip Radlinski and Nick Craswell . 2017. A Theoretical Framework for Conversational Search. CHIIR. ACM, 117--126.

Digital Library

Google Scholar

[7]

Fabrizio Silvestri . 2010. Mining Query Logs: Turning Search Usage Data into Knowledge. Foundations and Trends in Information Retrieval, Vol. 4, 1--2 (2010), 1--174.

Digital Library

Google Scholar

[8]

Johanne R. Trippas, Damiano Spina, Lawrence Cavedon, Hideo Joho, and Mark Sanderson . 2018. Informing the Design of Spoken Conversational Search: Perspective Paper CHIIR. ACM, 32--41.

Digital Library

Google Scholar

[9]

Johanne R. Trippas, Damiano Spina, Lawrence Cavedon, and Mark Sanderson . 2017. How Do People Interact in Conversational Speech-Only Search Tasks: A Preliminary Analysis. In CHIIR. ACM, 325--328.

Digital Library

Google Scholar

[10]

Alexandra Vtyurina, Denis Savenkov, Eugene Agichtein, and Charles L. A. Clarke . 2017. Exploring Conversational Search With Humans, Assistants, and Wizards CHI Extended Abstracts. ACM, 2187--2193.

Digital Library

Google Scholar

Cited By

View all

Tobioka KYamamoto TOhshima HSakai TIshita EOhshima HHasibi FMao JJose J(2024)Timing of Aspect Suggestion to Encourage Diverse Information Acquisition in Spoken Conversational SearchProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698418(145-153)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698418
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
Ji KPathiyan Cherumanal STrippas JHettiachchi DSalim FScholer FSpina D(2024)Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational SearchAdjunct Proceedings of the 26th International Conference on Mobile Human-Computer Interaction10.1145/3640471.3680245(1-10)Online publication date: 21-Sep-2024
https://dl.acm.org/doi/10.1145/3640471.3680245
Show More Cited By

Index Terms

Toward Voice Query Clarification
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
      1. Query intent
    2. Users and interactive retrieval
      1. Search interfaces

Recommendations

Clarifying False Memories in Voice-based Search
CHIIR '19: Proceedings of the 2019 Conference on Human Information Interaction and Retrieval

Queries containing false memories (i.e., attributes the user misremembered about a searched item) represent a challenge for search systems. A query with a false memory will match inadequate results or even no result, and an automatic query correction is ...
The Characteristics of Voice Search: Comparing Spoken with Typed-in Mobile Web Search Queries

The growing popularity of mobile search and the advancement in voice recognition technologies have opened the door for web search users to speak their queries rather than type them. While this kind of voice search is still in its infancy, it is ...
Interactive Query Clarification and Refinement via User Simulation
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

When users initiate search sessions, their query are often ambiguous or might lack of context; this resulting in non-efficient document ranking. Multiple approaches have been proposed by the Information Retrieval community to add context and retrieve ...

Comments

Information & Contributors

Information

Published In

SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

June 2018

1509 pages

ISBN:9781450356572

DOI:10.1145/3209978

General Chairs:
Kevyn Collins-Thompson
University of Michigan, United States
,
Qiaozhu Mei
University of Michigan, United States
,
Program Chairs:
Brian Davison
Lehigh University, United States
,
Yiqun Liu
Tsinghua University, China
,
Emine Yilmaz
University College London, United Kingdom

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 June 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

SIGIR '18

Sponsor:

SIGIR

SIGIR '18: The 41st International ACM SIGIR conference on research and development in Information Retrieval

July 8 - 12, 2018

MI, Ann Arbor, USA

Acceptance Rates

SIGIR '18 Paper Acceptance Rate 86 of 409 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

34
Total Citations
View Citations
401
Total Downloads

Downloads (Last 12 months)21
Downloads (Last 6 weeks)1

Reflects downloads up to 24 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Tobioka KYamamoto TOhshima HSakai TIshita EOhshima HHasibi FMao JJose J(2024)Timing of Aspect Suggestion to Encourage Diverse Information Acquisition in Spoken Conversational SearchProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698418(145-153)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698418
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
Ji KPathiyan Cherumanal STrippas JHettiachchi DSalim FScholer FSpina D(2024)Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational SearchAdjunct Proceedings of the 26th International Conference on Mobile Human-Computer Interaction10.1145/3640471.3680245(1-10)Online publication date: 21-Sep-2024
https://dl.acm.org/doi/10.1145/3640471.3680245
Yuan YSiro CAliannejadi MRijke MLam WChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational SearchProceedings of the ACM Web Conference 202410.1145/3589334.3645483(1474-1485)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645483
Beelen TOrdelman RTruong KEvers VHuibers T(2024)Does conversation lead to better searches? Investigating single-shot and multi-turn spoken searches with childrenInternational Journal of Child-Computer Interaction10.1016/j.ijcci.2024.10066841:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.ijcci.2024.100668
Bedi NSekulić ICrestani F(2024)Clarifying Questions Generation for Conversational Search Based on “People Also Ask” FeatureSustainability and Empowerment in the Context of Digital Libraries10.1007/978-981-96-0865-2_20(246-260)Online publication date: 6-Dec-2024
https://doi.org/10.1007/978-981-96-0865-2_20
Aliannejadi MGwizdka JZamani H(2024)Interactions with Generative Information Retrieval SystemsInformation Access in the Era of Generative AI10.1007/978-3-031-73147-1_3(47-71)Online publication date: 12-Sep-2024
https://doi.org/10.1007/978-3-031-73147-1_3
Sekulić IŁajewska WBalog KCrestani F(2024)Estimating the Usefulness of Clarifying Questions and Answers for Conversational SearchAdvances in Information Retrieval10.1007/978-3-031-56063-7_30(384-392)Online publication date: 23-Mar-2024
https://doi.org/10.1007/978-3-031-56063-7_30
Kiesel JGohsen MMirzakhmedova NHagen MStein B(2024)Simulating Follow-Up Questions in Conversational SearchAdvances in Information Retrieval10.1007/978-3-031-56060-6_25(382-398)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56060-6_25
Ghosh SShah C(2024)Spoken conversational search: Evaluating the effect of system clarifications on user experience through Wizard‐of‐Oz studyJournal of the Association for Information Science and Technology10.1002/asi.24974Online publication date: 29-Dec-2024
https://doi.org/10.1002/asi.24974
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Clarifying False Memories in Voice-based Search

The Characteristics of Voice Search: Comparing Spoken with Typed-in Mobile Web Search Queries

Interactive Query Clarification and Refinement via User Simulation

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations