A survey of discourse parsing

Abstract

Discourse parsing is an important research area in natural language processing (NLP), which aims to parse the discourse structure of coherent sentences. In this survey, we introduce several different kinds of discourse parsing tasks, mainly including RST-style discourse parsing, PDTB-style discourse parsing, and discourse parsing for multiparty dialogue. For these tasks, we introduce the classical and recent existing methods, especially neural network approaches. After that, we describe the applications of discourse parsing for other NLP tasks, such as machine reading comprehension and sentiment analysis. Finally, we discuss the future trends of the task.

Graphical abstract

Keywords

discourse parsing / discourse structure / RST / PDTB / STAC

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Acknowledgements

The research in this article is supported by the Science and Technology Innovation 2030 -“New Generation Artificial Intelligence” Major Project (2018AA0101901), the National Key Research and Development Project (2018YFB1005103), the National Natural Science Foundation of China (Grant Nos. 61772156 and 61976073), Shenzhen Foundational Research Funding (JCYJ20200109113441941), and the Foundation of Heilongjiang Province (F2018013).

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