Top-down Discourse Parsing via Sequence Labelling

Fajri Koto, Jey Han Lau, Timothy Baldwin


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.
Anthology ID:
2021.eacl-main.60
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
715–726
Language:
URL:
https://aclanthology.org/2021.eacl-main.60
DOI:
10.18653/v1/2021.eacl-main.60
Bibkey:
Cite (ACL):
Fajri Koto, Jey Han Lau, and Timothy Baldwin. 2021. Top-down Discourse Parsing via Sequence Labelling. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 715–726, Online. Association for Computational Linguistics.
Cite (Informal):
Top-down Discourse Parsing via Sequence Labelling (Koto et al., EACL 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.eacl-main.60.pdf
Code
 fajri91/NeuralRST-TopDown
Data
RST-DT