ESURF, a simple random forest with lexical and character n-gram features, reaches state-of-the-art EDU segmentation on RST-DT and modestly improves RST parsing.
Top-down Discourse Parsing via Sequence Labelling
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abstract
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.
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ESURF: Simple and Effective EDU Segmentation
ESURF, a simple random forest with lexical and character n-gram features, reaches state-of-the-art EDU segmentation on RST-DT and modestly improves RST parsing.