We show that using the rhetorical structure automatically generated by the discourse parser is beneficial for paragraph-level argument mining in Russian. First, we improve the structure awareness of the current RST discourse parser for Russian by employing the recent top-down approach for unlabeled tree construction on a paragraph level. Then we demonstrate the utility of this parser in two classification argument mining subtasks of the RuARG-2022 shared task. Our approach leverages a structured LSTM module to compute a text representation that reflects the composition of discourse units in the rhetorical structure. We show that: (i) the inclusion of discourse analysis improves paragraph-level text classification; (ii) a novel TreeLSTM-based approach performs well for the computation of the complex text hidden representation using both a language model and an end-to-end RST parser; (iii) structures predicted by the proposed RST parser reflect the argumentative structures in texts in Russian.
Download PDF from the Dialogue conference website: https://www.dialog-21.ru/media/5753/chistovaeplussmirnovi028.pdf
Download the collection of proceedings (PDF) from the Dialogue conference website: https://www.dialog-21.ru/media/5847/_-dialog2022scopus.pdf
Elena Chistova and Ivan Smirnov. Discourse-aware text classification for argument mining // Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2022”. Issue 21. Pp. 93–105.