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Improved Discourse Parsing with Two-Step Neural Transition-Based Model

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Published:11 January 2018Publication History
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Abstract

Discourse parsing aims to identify structures and relationships between different discourse units. Most existing approaches analyze a whole discourse at once, which often fails in distinguishing long-span relations and properly representing discourse units. In this article, we propose a novel parsing model to analyze discourse in a two-step fashion with different feature representations to characterize intra sentence and inter sentence discourse structures, respectively. Our model works in a transition-based framework and benefits from a stack long short-term memory neural network model. Experiments on benchmark tree banks show that our method outperforms traditional 1-step parsing methods in both English and Chinese.

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    • Published in

      cover image ACM Transactions on Asian and Low-Resource Language Information Processing
      ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 17, Issue 2
      June 2018
      134 pages
      ISSN:2375-4699
      EISSN:2375-4702
      DOI:10.1145/3160862
      Issue’s Table of Contents

      Copyright © 2018 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 11 January 2018
      • Accepted: 1 October 2017
      • Revised: 1 August 2017
      • Received: 1 April 2017
      Published in tallip Volume 17, Issue 2

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