Abstract
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.
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Acknowledgements
This work was partially supported by the Ministry of Science and Higher Education of the Russian Federation, project No. 075-15-2020-799.
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Appendix
Example of segment underprediction (translation is in Fig. 5).
Example of segment overprediction (translation is in Fig. 6).
Translated example of segment underprediction.
Translated example of segment overprediction.
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Chistova, E. et al. (2021). RST Discourse Parser for Russian: An Experimental Study of Deep Learning Models. In: van der Aalst, W.M.P., et al. Analysis of Images, Social Networks and Texts. AIST 2020. Lecture Notes in Computer Science(), vol 12602. Springer, Cham. https://doi.org/10.1007/978-3-030-72610-2_8
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