@inproceedings{huber-etal-2021-w,
title = "{W}-{RST}: Towards a Weighted {RST}-style Discourse Framework",
author = "Huber, Patrick and
Xiao, Wen and
Carenini, Giuseppe",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.302/",
doi = "10.18653/v1/2021.acl-long.302",
pages = "3908--3918",
abstract = "Aiming for a better integration of data-driven and linguistically-inspired approaches, we explore whether RST Nuclearity, assigning a binary assessment of importance between text segments, can be replaced by automatically generated, real-valued scores, in what we call a Weighted-RST framework. In particular, we find that weighted discourse trees from auxiliary tasks can benefit key NLP downstream applications, compared to nuclearity-centered approaches. We further show that real-valued importance distributions partially and interestingly align with the assessment and uncertainty of human annotators."
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<abstract>Aiming for a better integration of data-driven and linguistically-inspired approaches, we explore whether RST Nuclearity, assigning a binary assessment of importance between text segments, can be replaced by automatically generated, real-valued scores, in what we call a Weighted-RST framework. In particular, we find that weighted discourse trees from auxiliary tasks can benefit key NLP downstream applications, compared to nuclearity-centered approaches. We further show that real-valued importance distributions partially and interestingly align with the assessment and uncertainty of human annotators.</abstract>
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%0 Conference Proceedings
%T W-RST: Towards a Weighted RST-style Discourse Framework
%A Huber, Patrick
%A Xiao, Wen
%A Carenini, Giuseppe
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F huber-etal-2021-w
%X Aiming for a better integration of data-driven and linguistically-inspired approaches, we explore whether RST Nuclearity, assigning a binary assessment of importance between text segments, can be replaced by automatically generated, real-valued scores, in what we call a Weighted-RST framework. In particular, we find that weighted discourse trees from auxiliary tasks can benefit key NLP downstream applications, compared to nuclearity-centered approaches. We further show that real-valued importance distributions partially and interestingly align with the assessment and uncertainty of human annotators.
%R 10.18653/v1/2021.acl-long.302
%U https://aclanthology.org/2021.acl-long.302/
%U https://doi.org/10.18653/v1/2021.acl-long.302
%P 3908-3918
Markdown (Informal)
[W-RST: Towards a Weighted RST-style Discourse Framework](https://aclanthology.org/2021.acl-long.302/) (Huber et al., ACL-IJCNLP 2021)
ACL
- Patrick Huber, Wen Xiao, and Giuseppe Carenini. 2021. W-RST: Towards a Weighted RST-style Discourse Framework. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3908–3918, Online. Association for Computational Linguistics.