2019
ACL
ACL 2019
A Unified Linear-Time Framework for Sentence-Level Discourse Parsing
Abstract
AbstractWe propose an efficient neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory (RST). Our framework comprises a discourse segmenter to identify the elementary discourse units (EDU) in a text, and a discourse parser that constructs a discourse tree in a top-down fashion. Both the segmenter and the parser are based on Pointer Networks and operate in linear time. Our segmenter yields an F1 score of 95.4%, and our parser achieves an F1 score of 81.7% on the aggregated labeled (relation) metric, surpassing previous approaches by a good margin and approaching human agreement on both tasks (98.3 and 83.0 F1).
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Interdisciplinary Bridge
— Computer Science and Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— linear time parsing
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Deep Learning > Architectures > Neural Networks
Computer Science > Applications > Document Analysis
Natural Language Processing > Applications > Text Generation
Machine Learning > Learning Paradigms > Self-Supervised Learning
Natural Language Processing > Applications > Text Processing