Top-down Discourse Parsing via Sequence Labelling

• URL: http://arxiv.org/abs/2102.02080v1
• Date: Wed, 3 Feb 2021 14:30:21 GMT
• Title: Top-down Discourse Parsing via Sequence Labelling
• Authors: Fajri Koto and Jey Han Lau and Timothy Baldwin
• Abstract summary: We introduce a top-down approach to discourse parsing that is conceptually simpler than its predecessors. By framing the task as a sequence labelling problem, we are able to eliminate the decoder and reduce the search space for splitting points. Based on the Full metric, our proposed LSTM model sets a new state-of-the-art for RST parsing.
• Score: 33.46519116869276
• License: http://creativecommons.org/licenses/by/4.0/
• 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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