@inproceedings{liu-etal-2020-multilingual-neural,
title = "Multilingual Neural {RST} Discourse Parsing",
author = "Liu, Zhengyuan and
Shi, Ke and
Chen, Nancy",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.591",
doi = "10.18653/v1/2020.coling-main.591",
pages = "6730--6738",
abstract = "Text discourse parsing plays an important role in understanding information flow and argumentative structure in natural language. Previous research under the Rhetorical Structure Theory (RST) has mostly focused on inducing and evaluating models from the English treebank. However, the parsing tasks for other languages such as German, Dutch, and Portuguese are still challenging due to the shortage of annotated data. In this work, we investigate two approaches to establish a neural, cross-lingual discourse parser via: (1) utilizing multilingual vector representations; and (2) adopting segment-level translation of the source content. Experiment results show that both methods are effective even with limited training data, and achieve state-of-the-art performance on cross-lingual, document-level discourse parsing on all sub-tasks.",
}
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%0 Conference Proceedings
%T Multilingual Neural RST Discourse Parsing
%A Liu, Zhengyuan
%A Shi, Ke
%A Chen, Nancy
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F liu-etal-2020-multilingual-neural
%X Text discourse parsing plays an important role in understanding information flow and argumentative structure in natural language. Previous research under the Rhetorical Structure Theory (RST) has mostly focused on inducing and evaluating models from the English treebank. However, the parsing tasks for other languages such as German, Dutch, and Portuguese are still challenging due to the shortage of annotated data. In this work, we investigate two approaches to establish a neural, cross-lingual discourse parser via: (1) utilizing multilingual vector representations; and (2) adopting segment-level translation of the source content. Experiment results show that both methods are effective even with limited training data, and achieve state-of-the-art performance on cross-lingual, document-level discourse parsing on all sub-tasks.
%R 10.18653/v1/2020.coling-main.591
%U https://aclanthology.org/2020.coling-main.591
%U https://doi.org/10.18653/v1/2020.coling-main.591
%P 6730-6738
Markdown (Informal)
[Multilingual Neural RST Discourse Parsing](https://aclanthology.org/2020.coling-main.591) (Liu et al., COLING 2020)
ACL
- Zhengyuan Liu, Ke Shi, and Nancy Chen. 2020. Multilingual Neural RST Discourse Parsing. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6730–6738, Barcelona, Spain (Online). International Committee on Computational Linguistics.