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Machine Comprehension with Discourse Relations

Karthik Narasimhan,R. Barzilay

Published 2015 in Annual Meeting of the Association for Computational Linguistics

ABSTRACT

This paper proposes a novel approach for incorporating discourse information into machine comprehension applications. Traditionally, such information is computed using off-the-shelf discourse analyzers. This design provides limited opportunities for guiding the discourse parser based on the requirements of the target task. In contrast, our model induces relations between sentences while optimizing a task-specific objective. This approach enables the model to benefit from discourse information without relying on explicit annotations of discourse structure during training. The model jointly identifies relevant sentences, establishes relations between them and predicts an answer. We implement this idea in a discriminative framework with hidden variables that capture relevant sentences and relations unobserved during training. Our experiments demonstrate that the discourse aware model outperforms state-of-the-art machine comprehension systems.1

PUBLICATION RECORD

  • Publication year

    2015

  • Venue

    Annual Meeting of the Association for Computational Linguistics

  • Publication date

    Unknown publication date

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.3115/v1/P15-1121
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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