@inproceedings{kobayashi-etal-2022-simple,
title = "A Simple and Strong Baseline for End-to-End Neural {RST}-style Discourse Parsing",
author = "Kobayashi, Naoki and
Hirao, Tsutomu and
Kamigaito, Hidetaka and
Okumura, Manabu and
Nagata, Masaaki",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.501",
doi = "10.18653/v1/2022.findings-emnlp.501",
pages = "6725--6737",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
%A Kobayashi, Naoki
%A Hirao, Tsutomu
%A Kamigaito, Hidetaka
%A Okumura, Manabu
%A Nagata, Masaaki
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F kobayashi-etal-2022-simple
%X 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.
%R 10.18653/v1/2022.findings-emnlp.501
%U https://aclanthology.org/2022.findings-emnlp.501
%U https://doi.org/10.18653/v1/2022.findings-emnlp.501
%P 6725-6737
Markdown (Informal)
[A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing](https://aclanthology.org/2022.findings-emnlp.501) (Kobayashi et al., Findings 2022)
ACL