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Reinforcement Learning Based Argument Component Detection

Yang Gao,Hao Wang,Chen Zhang,Wei Wang

Published 2017 in arXiv.org

ABSTRACT

Argument component detection (ACD) is an important sub-task in argumentation mining. ACD aims at detecting and classifying different argument components in natural language texts. Historical annotations (HAs) are important features the human annotators consider when they manually perform the ACD task. However, HAs are largely ignored by existing automatic ACD techniques. Reinforcement learning (RL) has proven to be an effective method for using HAs in some natural language processing tasks. In this work, we propose a RL-based ACD technique, and evaluate its performance on two well-annotated corpora. Results suggest that, in terms of classification accuracy, HAs-augmented RL outperforms plain RL by at most 17.85%, and outperforms the state-of-the-art supervised learning algorithm by at most 11.94%.

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    arXiv.org

  • Publication date

    2017-02-21

  • Fields of study

    Computer Science

  • Identifiers
    arXiv 1702.06239
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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