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Search Clarification Selection via Query-Intent-Clarification Graph Attention

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Advances in Information Retrieval (ECIR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13185))

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Abstract

Proactively asking clarifications in response to search queries is a useful technique for revealing the intent of the query. Search clarification is important for both web and conversational search. This paper focuses on the clarification selection task. Inspired by the fact that a good clarification should clarify the query’s different intents, we propose a graph attention-based clarification selection model that can exploit the relations among a given query, its intents, and its clarifications via constructing a query-intent-clarification attention graph. The comparison with competitive baselines on large-scale search clarification data demonstrates the effectiveness of our model.

The work described in this paper is substantially supported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: 14200620).

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Authors and Affiliations

  1. The Chinese University of Hong Kong, Shatin, Hong Kong

    Chang Gao & Wai Lam

Authors
  1. Chang Gao
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  2. Wai Lam
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Correspondence to Chang Gao or Wai Lam .

Editor information

Editors and Affiliations

  1. Martin Luther University Halle-Wittenberg, Halle, Germany

    Matthias Hagen

  2. Leiden University, Leiden, The Netherlands

    Suzan Verberne

  3. University of Glasgow, Glasgow, UK

    Craig Macdonald

  4. University of Duisburg-Essen, Essen, Germany

    Christin Seifert

  5. University of Stavanger, Stavanger, Norway

    Krisztian Balog

  6. Norwegian University of Science and Technology, Trondheim, Norway

    Kjetil Nørvåg

  7. University of Stavanger, Stavanger, Norway

    Vinay Setty

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Gao, C., Lam, W. (2022). Search Clarification Selection via Query-Intent-Clarification Graph Attention. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13185. Springer, Cham. https://doi.org/10.1007/978-3-030-99736-6_16

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  • DOI: https://doi.org/10.1007/978-3-030-99736-6_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-99735-9

  • Online ISBN: 978-3-030-99736-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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