Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining

Grigorii Guz, Patrick Huber, Giuseppe Carenini


Abstract
RST-based discourse parsing is an important NLP task with numerous downstream applications, such as summarization, machine translation and opinion mining. In this paper, we demonstrate a simple, yet highly accurate discourse parser, incorporating recent contextual language models. Our parser establishes the new state-of-the-art (SOTA) performance for predicting structure and nuclearity on two key RST datasets, RST-DT and Instr-DT. We further demonstrate that pretraining our parser on the recently available large-scale “silver-standard” discourse treebank MEGA-DT provides even larger performance benefits, suggesting a novel and promising research direction in the field of discourse analysis.
Anthology ID:
2020.coling-main.337
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3794–3805
Language:
URL:
https://aclanthology.org/2020.coling-main.337
DOI:
10.18653/v1/2020.coling-main.337
Bibkey:
Cite (ACL):
Grigorii Guz, Patrick Huber, and Giuseppe Carenini. 2020. Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3794–3805, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining (Guz et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.337.pdf
Data
Instructional-DT (Instr-DT)RST-DT