Discourse-aware text classification for argument mining

Elena Chistova, Ivan Smirnov

Abstract: 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.