This paper proposes an encoder-decoder structure, where the encoder represents the input span of words and EDUs using a hierarchical biLSTM. There are two level of sequences, first level is words contained in a EDU and second level is EDU sequences.

Transition-based Neural RST Parsing with Implicit Syntax Features

University of Michigan - Ann Arbor

EDU segmentation extracts the start and end of each EDU. The output should be segmentation of sentences. Modern systems generally use neural sequence models supervised by the gold EDU segmentation in datasets like the RST Discourse Treebank. A classic method can be to predict whether there is a break for every token.

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