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RST Discourse Parsing with Tree-Structured Neural Networks

  • Longyin Zhang
  • Cheng Sun
  • Xin Tan
  • Fang KongEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 954)

Abstract

Discourse structure has a central role in several NLP tasks, such as document translation, text summarization and dialogue generation. Also, text-level discourse parsing is notoriously difficult for the long distance of discourse and deep structures of discourse trees. In this paper, we build a tree-structured neural network for RST discourse parsing. We also introduce two tracking LSTMs to store long-distance information of a document to strengthen the representations for sentences and the entire document. Experimental results show that our proposed method obtains comparable performance regarding standard discourse parsing evaluations when compared with state-of-the-art systems.

Keywords

Discourse parsing RST-DT Tree-structured LSTM 

Notes

Acknowledgements

The authors would like to thank three anonymous reviewers for their comments on this paper. This research was supported by the General Program of the National Natural Science Foundation of China under Grant No. 61472264, the Artificial Intelligence Emergency Project under Grant No. 61751206, the sub-topic of the National Key Research and Development Program under Grant No. 2017YFB1002101, and the Young Scientists Fund of the National Natural Science Foundation of China under Grant No. 61502149.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Natural Language Processing Lab, School of Computer Science and TechnologySoochow UniversitySuzhouChina

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