RST Parsing from Scratch

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RST Parsing from Scratch
Title:
RST Parsing from Scratch
Other Titles:
Annual Conference of the North American Chapter of the Association for Computational Linguistics
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Publication Date:
11 June 2021
Citation:
Thanh-Tung Nguyen, Xuan-Phi Nguyen, Shafiq Joty, and Xiaoli Li. In Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL'21) , 2021.
Abstract:
We introduce a novel top-down end-to-end formulation of document level discourse parsing in the Rhetorical Structure Theory (RST) framework. In this formulation, we consider discourse parsing as a sequence of splitting decisions at token boundaries and use a seq2seq network to model the splitting decisions. Our framework facilitates discourse parsing from scratch without requiring discourse segmentation as a prerequisite; rather, it yields segmentation as part of the parsing process. Our unified parsing model adopts a beam search to decode the best tree structure by searching through a space of high scoring trees. With extensive experiments on the standard RST discourse treebank, we demonstrate that our parser outperforms existing methods by a good margin in both end-to-end parsing and parsing with gold segmentation. More importantly, it does so without using any handcrafted features, making it faster and easily adaptable to new languages and domains.
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Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research is supported by core funding from: Institute for Infocomm Research
Grant Reference no. : Nil
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Nil
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