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Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz)

KI 2018: KI 2018: Advances in Artificial Intelligence pp 315–326Cite as

A Variant of Monte-Carlo Tree Search for Referring Expression Generation

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11117))

Abstract

In natural language generation, the task of Referring Expression Generation (REG) is to determine a set of features or relations which identify a target object. Referring expressions describe the target object and discriminate it from other objects in a scene. From an algorithmic point of view, REG can be posed as a search problem. Since search space is exponential with respect to the number of features and relations available, efficient search strategies are required. In this paper we investigate variants of Monte-Carlo Tree Search (MCTS) for application in REG. We propose a new variant, called Quasi Best-First MCTS (QBF-MCTS). In an empirical study we compare different MCTS variants to one another, and to classic REG algorithms. The results indicate that QBF-MCTS yields a significantly improved performance with respect to efficiency and quality.

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Author information

Authors and Affiliations

  1. University of Bamberg, Bamberg, Germany

    Tobias Schwartz & Diedrich Wolter

Authors
  1. Tobias Schwartz
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  2. Diedrich Wolter
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Correspondence to Diedrich Wolter .

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Editors and Affiliations

  1. TU Berlin, Berlin, Germany

    Frank Trollmann

  2. TU Dresden, Dresden, Germany

    Anni-Yasmin Turhan

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Schwartz, T., Wolter, D. (2018). A Variant of Monte-Carlo Tree Search for Referring Expression Generation. In: Trollmann, F., Turhan, AY. (eds) KI 2018: Advances in Artificial Intelligence. KI 2018. Lecture Notes in Computer Science(), vol 11117. Springer, Cham. https://doi.org/10.1007/978-3-030-00111-7_27

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  • DOI: https://doi.org/10.1007/978-3-030-00111-7_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00110-0

  • Online ISBN: 978-3-030-00111-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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