@inproceedings{koto-etal-2021-top,
title = "Top-down Discourse Parsing via Sequence Labelling",
author = "Koto, Fajri and
Lau, Jey Han and
Baldwin, Timothy",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.60",
doi = "10.18653/v1/2021.eacl-main.60",
pages = "715--726",
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.",
}
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%0 Conference Proceedings
%T Top-down Discourse Parsing via Sequence Labelling
%A Koto, Fajri
%A Lau, Jey Han
%A Baldwin, Timothy
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F koto-etal-2021-top
%X 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.
%R 10.18653/v1/2021.eacl-main.60
%U https://aclanthology.org/2021.eacl-main.60
%U https://doi.org/10.18653/v1/2021.eacl-main.60
%P 715-726
Markdown (Informal)
[Top-down Discourse Parsing via Sequence Labelling](https://aclanthology.org/2021.eacl-main.60) (Koto et al., EACL 2021)
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.