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

Авторы

Смирнов И. В. Чистова Е. В. Кобозева М. В.

Аннотация

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.

Внешние ссылки

DOI: 10.1007/978-3-030-72610-2_8

ResearchGate: https://www.researchgate.net/publication/350745602_RST_Discourse_Parser_for_Russian_An_Experimental_Study_of_Deep_Learning_Models

Смотреть презентацию Елены Чистовой на канале конференции AIST (англ.):

Ссылка при цитировании

Chistova E., Shelmanov A., Pisarevskaya D., Kobozeva M., Isakov V., Panchenko A., Toldova S., Smirnov I. RST Discourse Parser for Russian: An Experimental Study of Deep Learning Models // International Conference on Analysis of Images, Social Networks and Texts. — Lecture Notes in Computer Science, vol 12602, Springer, Cham, 2021, pp. 105-119.