Neural Discourse Structure for Text Categorization

ACL 2017

Neural Discourse Structure for Text Categorization

Jan 25, 2021
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Abstract: We show that discourse structure, as defined by Rhetorical Structure Theory and provided by an existing discourse parser, benefits text categorization. Our approach uses a recursive neural network and a newly proposed attention mechanism to compute a representation of the text that focuses on salient content, from the perspective of both RST and the task. Experiments consider variants of the approach and illustrate its strengths and weaknesses. Authors: Yangfeng Ji, Noah Smith (University of Washington)

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