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 features, making it
faster and easily adaptable to new languages and domains.

本文提出了一种基于分裂决策的顶级自上而下的端到端文档级修辞结构理论（RST）框架下的论述解析方式，并采用 seq2seq 网络建模分离决策，无需依赖分段，融合高分树搜索算法得到最佳树结构，实验结果表明该解析器在端对端解析和使用黄金分段分析方面表现出色，且无需使用手工特征，速度更快，易于适应新的语言和领域。