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Query Performance Prediction for Neural IR: Are We There Yet?

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Advances in Information Retrieval (ECIR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13980))

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Abstract

Evaluation in Information Retrieval (IR) relies on post-hoc empirical procedures, which are time-consuming and expensive operations. To alleviate this, Query Performance Prediction (QPP) models have been developed to estimate the performance of a system without the need for human-made relevance judgements. Such models, usually relying on lexical features from queries and corpora, have been applied to traditional sparse IR methods – with various degrees of success. With the advent of neural IR and large Pre-trained Language Models, the retrieval paradigm has significantly shifted towards more semantic signals. In this work, we study and analyze to what extent current QPP models can predict the performance of such systems. Our experiments consider seven traditional bag-of-words and seven BERT-based IR approaches, as well as nineteen state-of-the-art QPPs evaluated on two collections, Deep Learning ’19 and Robust ’04. Our findings show that QPPs perform statistically significantly worse on neural IR systems. In settings where semantic signals are prominent (e.g., passage retrieval), their performance on neural models drops by as much as 10% compared to bag-of-words approaches. On top of that, in lexical-oriented scenarios, QPPs fail to predict performance for neural IR systems on those queries where they differ from traditional approaches the most.

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Notes

  1. 1.

    We use the implementation provided at https://github.com/Narabzad/BERTQPP.

  2. 2.

    Additional IR measures and correlations, as well as full ANOVA tables are available at: https://github.com/guglielmof/ECIR2023-QPP.

  3. 3.

    To avoid cluttering, we report the subsequent analyses only for post-retrieval predictors – similar observations hold for pre-retrieval ones.

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Acknowledgements

The work was partially supported by University of Padova Strategic Research Infrastructure Grant 2017: “CAPRI: Calcolo ad Alte Pre-stazioni per la Ricerca e l’Innovazione”, ExaMode project, as part of the EU H2020 program under Grant Agreement no. 825292.

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Authors and Affiliations

  1. University of Padova, Padova, Italy

    Guglielmo Faggioli, Stefano Marchesin & Nicola Ferro

  2. Naver Labs Europe, Meylan, France

    Thibault Formal & Stéphane Clinchant

  3. Sorbonne Université, ISIR, Paris, France

    Thibault Formal & Benjamin Piwowarski

  4. CNRS, Paris, France

    Benjamin Piwowarski

Authors
  1. Guglielmo Faggioli
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  2. Thibault Formal
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  3. Stefano Marchesin
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  4. Stéphane Clinchant
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  5. Nicola Ferro
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  6. Benjamin Piwowarski
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Correspondence to Guglielmo Faggioli .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  2. Université Grenoble-Alpes, Saint-Martin-d’Hères, France

    Lorraine Goeuriot

  3. Università della Svizzera Italiana, Lugano, Switzerland

    Fabio Crestani

  4. University of Copenhagen, Copenhagen, Denmark

    Maria Maistro

  5. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  6. Dublin City University, Dublin, Ireland

    Brian Davis

  7. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  8. Universität Regensburg, Regensburg, Germany

    Udo Kruschwitz

  9. Dublin City University, Dublin, Ireland

    Annalina Caputo

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Faggioli, G., Formal, T., Marchesin, S., Clinchant, S., Ferro, N., Piwowarski, B. (2023). Query Performance Prediction for Neural IR: Are We There Yet?. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13980. Springer, Cham. https://doi.org/10.1007/978-3-031-28244-7_15

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  • DOI: https://doi.org/10.1007/978-3-031-28244-7_15

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