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Examining Additivity and Weak Baselines

Authors:

Sadegh Kharazmi,

Mark SandersonAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 34, Issue 4

Article No.: 23, Pages 1 - 18

https://doi.org/10.1145/2882782

Published: 09 June 2016 Publication History

Abstract

We present a study of which baseline to use when testing a new retrieval technique. In contrast to past work, we show that measuring a statistically significant improvement over a weak baseline is not a good predictor of whether a similar improvement will be measured on a strong baseline. Sometimes strong baselines are made worse when a new technique is applied. We investigate whether conducting comparisons against a range of weaker baselines can increase confidence that an observed effect will also show improvements on a stronger baseline. Our results indicate that this is not the case -- at best, testing against a range of baselines means that an experimenter can be more confident that the new technique is unlikely to significantly harm a strong baseline. Examining recent past work, we present evidence that the information retrieval (IR) community continues to test against weak baselines. This is unfortunate as, in light of our experiments, we conclude that the only way to be confident that a new technique is a contribution is to compare it against nothing less than the state of the art.

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Cited By

Ferro N(2024)What Happened in CLEF$$\ldots $$ For Another While?Experimental IR Meets Multilinguality, Multimodality, and Interaction10.1007/978-3-031-71736-9_1(3-57)Online publication date: 14-Sep-2024
https://doi.org/10.1007/978-3-031-71736-9_1
Alessio MFaggioli GFerro NChen HDuh WHuang HKato MMothe JPoblete B(2023)DECAF: A Modular and Extensible Conversational Search FrameworkProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591913(3075-3085)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591913
Faggioli GFerro NMuntean CPerego RTonellotto NChen HDuh WHuang HKato MMothe JPoblete B(2023)A Geometric Framework for Query Performance Prediction in Conversational SearchProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591625(1355-1365)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591625
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Index Terms

Examining Additivity and Weak Baselines
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 34, Issue 4

September 2016

217 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/2954381

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

Issue’s Table of Contents

Copyright © 2016 ACM.

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Publication History

Published: 09 June 2016

Accepted: 01 January 2016

Revised: 01 November 2015

Received: 01 July 2015

Published in TOIS Volume 34, Issue 4

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Cited By

Ferro N(2024)What Happened in CLEF$$\ldots $$ For Another While?Experimental IR Meets Multilinguality, Multimodality, and Interaction10.1007/978-3-031-71736-9_1(3-57)Online publication date: 14-Sep-2024
https://doi.org/10.1007/978-3-031-71736-9_1
Alessio MFaggioli GFerro NChen HDuh WHuang HKato MMothe JPoblete B(2023)DECAF: A Modular and Extensible Conversational Search FrameworkProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591913(3075-3085)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591913
Faggioli GFerro NMuntean CPerego RTonellotto NChen HDuh WHuang HKato MMothe JPoblete B(2023)A Geometric Framework for Query Performance Prediction in Conversational SearchProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591625(1355-1365)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591625
Zhang PGao HHu ZYang MSong DWang JHou YHu B(2022)A bias–variance evaluation framework for information retrieval systemsInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10274759:1Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1016/j.ipm.2021.102747
Dang ELuk RAllan J(2021)A Comparison between Term-Independence Retrieval Models for Ad Hoc RetrievalACM Transactions on Information Systems10.1145/348361240:3(1-37)Online publication date: 8-Dec-2021
https://dl.acm.org/doi/10.1145/3483612
Albahem ASpina DScholer FCavedon L(2021)Component-based Analysis of Dynamic Search PerformanceACM Transactions on Information Systems10.1145/348323740:3(1-47)Online publication date: 22-Nov-2021
https://dl.acm.org/doi/10.1145/3483237
Han XLiu YLin JHasibi FFang YAizawa A(2021)The Simplest Thing That Can Possibly Work: (Pseudo-)Relevance Feedback via Text ClassificationProceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3471158.3472261(123-129)Online publication date: 11-Jul-2021
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Mackenzie JMoffat Ad'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Examining the Additivity of Top-k Query Processing InnovationsProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412000(1085-1094)Online publication date: 19-Oct-2020
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Yigit-Sert SAltingovde IMacdonald COunis IUlusoy Ö(2020)Supervised approaches for explicit search result diversificationInformation Processing & Management10.1016/j.ipm.2020.10235657:6(102356)Online publication date: Nov-2020
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Ulu YAltingovde I(2020)Predicting the Size of Candidate Document Set for Implicit Web Search Result DiversificationAdvances in Information Retrieval10.1007/978-3-030-45442-5_51(410-417)Online publication date: 14-Apr-2020
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