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Modeling User Search Tasks with a Language-Agnostic Unsupervised Approach

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Advances in Information Retrieval (ECIR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12656))

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Abstract

Conversational information seeking is a major emerging research area because of the increasing popularity of conversational AI systems users utilize to perform their search tasks. Search systems and multiple other user supporting applications benefit from modeling the search tasks users carry out to satisfy their information needs. Most existing search task modeling methods are monolingual, and few methods leverage user clicks even though clicked URLs are crucial for modeling user intent. We propose a language-agnostic, user intent aware approach to model search tasks from user interactions with search systems. The proposed approach leverages user intent modeling from clicked query-document pairs, latent representations of queries in a language-agnostic space, and graph-based clusteringto model search tasks in an unsupervised approach. Experimental results demonstrate the proposed approach outperforms recent work in search task modeling, supporting user queries in multiple languages. It can also produce search task modeling results in the order of milliseconds, an essential aspect for conversational systems and user support applications requiring realtime results.

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Notes

  1. 1.

    https://github.com/google-research/google-research/tree/master/scann.

  2. 2.

    https://networkx.github.io/.

  3. 3.

    https://tfhub.dev/google/LaBSE/1.

  4. 4.

    https://cloud.google.com/translate.

  5. 5.

    https://github.com/google/pygtrie.

  6. 6.

    https://github.com/nhirakawa/BM25.

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Acknowledgement

This work was supported by the Agence National de la Recherche (ANR), through project CoST, code ANR-18-CE23-0016.

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Authors and Affiliations

  1. IRIT UMR 5505, CNRS,U. de Toulouse, Toulouse, France

    Luis Lugo, Jose G. Moreno & Gilles Hubert

Authors
  1. Luis Lugo
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  2. Jose G. Moreno
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  3. Gilles Hubert
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Corresponding author

Correspondence to Luis Lugo .

Editor information

Editors and Affiliations

  1. Radboud University Nijmegen, Nijmegen, The Netherlands

    Djoerd Hiemstra

  2. Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium

    Marie-Francine Moens

  3. Toulouse Institute of Computer Science Research, Toulouse, France

    Josiane Mothe

  4. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Raffaele Perego

  5. Leipzig University, Leipzig, Germany

    Martin Potthast

  6. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Fabrizio Sebastiani

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Lugo, L., Moreno, J.G., Hubert, G. (2021). Modeling User Search Tasks with a Language-Agnostic Unsupervised Approach. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12656. Springer, Cham. https://doi.org/10.1007/978-3-030-72113-8_27

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  • DOI: https://doi.org/10.1007/978-3-030-72113-8_27

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  • Online ISBN: 978-3-030-72113-8

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