Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank
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| Authors | Kai Zhao, Liang Huang |
| Journal/Conference Name | EMNLP 2017 9 |
| Paper Category | Artificial Intelligence |
| Paper Abstract | Discourse parsing has long been treated as a stand-alone problem independent from constituency or dependency parsing. Most attempts at this problem are pipelined rather than end-to-end, sophisticated, and not self-contained: they assume gold-standard text segmentations (Elementary Discourse Units), and use external parsers for syntactic features. In this paper we propose the first end-to-end discourse parser that jointly parses in both syntax and discourse levels, as well as the first syntacto-discourse treebank by integrating the Penn Treebank with the RST Treebank. Built upon our recent span-based constituency parser, this joint syntacto-discourse parser requires no preprocessing whatsoever (such as segmentation or feature extraction), achieves the state-of-the-art end-to-end discourse parsing accuracy. |
| Date of publication | 2017 |
| Code Programming Language | Python |
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