Multilingual Neural RST Discourse Parsing

Zhengyuan Liu, Ke Shi, Nancy Chen


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.
Anthology ID:
2020.coling-main.591
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6730–6738
Language:
URL:
https://aclanthology.org/2020.coling-main.591
DOI:
10.18653/v1/2020.coling-main.591
Bibkey:
Cite (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.
Cite (Informal):
Multilingual Neural RST Discourse Parsing (Liu et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.591.pdf
Code
 seq-to-mind/DMRST_Parser