TL;DR
This paper presents a novel end-to-end RST discourse parser that models parsing as token boundary splitting decisions using a seq2seq network, enabling segmentation and parsing without handcrafted features.
Contribution
It introduces a top-down, sequence-to-sequence approach for discourse parsing that performs segmentation and tree construction simultaneously, without relying on prior segmentation.
Findings
Outperforms existing methods in end-to-end RST parsing
Does not require handcrafted features, improving speed and adaptability
Achieves better accuracy on the standard English RST discourse treebank
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 English 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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsSigmoid Activation · Tanh Activation · Long Short-Term Memory · Sequence to Sequence
