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arXiv · 2102.02080

Top-down Discourse Parsing via Sequence Labelling

Fajri Koto·Jey Han Lau·Timothy Baldwin
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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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Fajri Koto, Jey Han Lau, Timothy Baldwin. 2021-02-03. Top-down Discourse Parsing via Sequence Labelling. https://arxiv.org/abs/2102.02080

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