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Towards Explainable Search Results: A Listwise Explanation Generator

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James AllanAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 669 - 680

https://doi.org/10.1145/3477495.3532067

Published: 07 July 2022 Publication History

Abstract

It has been shown that the interpretability of search results is enhanced when query aspects covered by documents are explicitly provided. However, existing work on aspect-oriented explanation of search results explains each document independently. These explanations thus cannot describe the differences between documents. This issue is also true for existing models on query aspect generation. Furthermore, these models provide a single query aspect for each document, even though documents often cover multiple query aspects. To overcome these limitations, we propose LiEGe, an approach that jointly explains all documents in a search result list. LiEGe provides semantic representations at two levels of granularity -- documents and their tokens -- using different interaction signals including cross-document interactions. These allow listwise modeling of a search result list as well as the generation of coherent explanations for documents. To appropriately explain documents that cover multiple query aspects, we introduce two settings for search result explanation: comprehensive and novelty explanation generation. LiEGe is trained and evaluated for both settings. We evaluate LiEGe on datasets built from Wikipedia and real query logs of the Bing search engine. Our experimental results demonstrate that LiEGe outperforms all baselines, with improvements that are substantial and statistically significant.

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Cited By

Xu ZLamba HAi QTetreault JJaimes AOosterhuis HBast HXiong C(2024)CFE2: Counterfactual Editing for Search Result ExplanationProceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3664190.3672508(145-155)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3664190.3672508
Wallat JHinrichs HAnand ASerra ESpezzano F(2024)Causal Probing for Dual EncodersProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679556(2292-2303)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679556
Chen CEickhoff CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Evaluating Search System Explainability with Psychometrics and CrowdsourcingProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657796(1051-1061)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657796
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Index Terms

Towards Explainable Search Results: A Listwise Explanation Generator
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
      1. Query intent
    2. Retrieval models and ranking
      1. Information retrieval diversity
      2. Novelty in information retrieval

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

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https://dl.acm.org/doi/10.1145/3664190.3672508
Wallat JHinrichs HAnand ASerra ESpezzano F(2024)Causal Probing for Dual EncodersProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679556(2292-2303)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679556
Chen CEickhoff CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Evaluating Search System Explainability with Psychometrics and CrowdsourcingProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657796(1051-1061)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657796
Juneja PZhang WSmith-Renner ALamba HTetreault JJaimes A(2024)Dissecting users' needs for search result explanationsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642059(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642059
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Schedl MAnelli VLex ESchedl MAnelli VLex E(2024)Biases, Fairness, and Non-discriminationTechnical and Regulatory Perspectives on Information Retrieval and Recommender Systems10.1007/978-3-031-69978-8_3(29-67)Online publication date: 24-Oct-2024
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