A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing

Naoki Kobayashi, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, Masaaki Nagata


Abstract
To promote and further develop RST-style discourse parsing models, we need a strong baseline that can be regarded as a reference for reporting reliable experimental results. This paper explores a strong baseline by integrating existing simple parsing strategies, top-down and bottom-up, with various transformer-based pre-trained language models.The experimental results obtained from two benchmark datasets demonstrate that the parsing performance strongly relies on the pre-trained language models rather than the parsing strategies.In particular, the bottom-up parser achieves large performance gains compared to the current best parser when employing DeBERTa.We further reveal that language models with a span-masking scheme especially boost the parsing performance through our analysis within intra- and multi-sentential parsing, and nuclearity prediction.
Anthology ID:
2022.findings-emnlp.501
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6725–6737
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.501
DOI:
10.18653/v1/2022.findings-emnlp.501
Bibkey:
Cite (ACL):
Naoki Kobayashi, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, and Masaaki Nagata. 2022. A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6725–6737, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing (Kobayashi et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-emnlp.501.pdf