We introduce a top-down approach to discourse parsing that is conceptually simpler than its predecessors (Kobayashi et al., 2020; Zhang et al., 2020). By framing the task as a sequence labelling problem where the goal is to iteratively segment a document into individual discourse units, we are able to eliminate the decoder and reduce the search space for splitting points. We explore both traditional recurrent models and modern pre-trained transformer models for the task, and additionally introduce a novel dynamic oracle for top-down parsing. Based on the Full metric, our proposed LSTM model sets a new state-of-the-art for RST parsing.

翻译：我们引入了一种自上而下的方法来对论述进行自上而下的分析,这种方法在概念上比其前身(Kobayashi等人,2020年;Zhang等人,2020年)简单得多。 通过将任务设计成一个顺序标签问题,目的是将文件反复分割成单个讨论单元,我们就能消除解密器并减少分离点的搜索空间。 我们探索了传统重复模式和现代培训前变压器模型,并另外为自上而下的区分引入了一个新的动态符。 根据全指标,我们提议的LSTM模型为RST的剖析设定了新的最新技术。