RST Discourse Parser for Russian: An Experimental Study of Deep Learning Models

P. 105–119.

Chistova E., Shelmanov A., Pisarevskaya D., Kobozeva M., Isakov V., Panchenko А., Toldova S., Smirnov I.

This work presents the first fully-fledged discourse parser for
Russian based on the Rhetorical Structure Theory of Mann and Thompson
(1988). For the segmentation, discourse tree construction, and discourse
relation classification we employ deep learning models. With the
help of multiple word embedding techniques, the new state of the art
for discourse segmentation of Russian texts is achieved. We found that
the neural classifiers using contextual word representations outperform
previously proposed feature-based models for discourse relation classification.
By ensembling both methods, we are able to further improve the
performance of the discourse relation classification achieving the new
state of the art for Russian.

Keywords: нейронные сети, neural networks, rhetoric structure, discourse parser, теория риторических структур, word embeddings, векторные представления, дискурсивный парсер

In book

Analysis of Images, Social Networks and Texts: 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020, Revised Selected Papers

Vol. 12602. , Springer, 2021.