Andrew Shen

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2022

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Easy-First Bottom-Up Discourse Parsing via Sequence Labelling
Andrew Shen | Fajri Koto | Jey Han Lau | Timothy Baldwin
Proceedings of the 3rd Workshop on Computational Approaches to Discourse

We propose a novel unconstrained bottom-up approach for rhetorical discourse parsing based on sequence labelling of adjacent pairs of discourse units (DUs), based on the framework of Koto et al. (2021). We describe the unique training requirements of an unconstrained parser, and explore two different training procedures: (1) fixed left-to-right; and (2) random order in tree construction. Additionally, we introduce a novel dynamic oracle for unconstrained bottom-up parsing. Our proposed parser achieves competitive results for bottom-up rhetorical discourse parsing.
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