End-to-End Argument Mining over Varying Rhetorical Structures
This work addresses argument mining for researchers by improving analysis through multiple discourse variants, but it is incremental as it builds on existing methods for a specific domain.
The paper tackled the problem of inconsistent rhetorical parsing in argument mining by proposing a deep dependency parsing model that uses rhetorical relations and training data augmentations from paraphrases, achieving the first fully-fledged argument parsing results on the Russian version of the Microtexts corpus.
Rhetorical Structure Theory implies no single discourse interpretation of a text, and the limitations of RST parsers further exacerbate inconsistent parsing of similar structures. Therefore, it is important to take into account that the same argumentative structure can be found in semantically similar texts with varying rhetorical structures. In this work, the differences between paraphrases within the same argument scheme are evaluated from a rhetorical perspective. The study proposes a deep dependency parsing model to assess the connection between rhetorical and argument structures. The model utilizes rhetorical relations; RST structures of paraphrases serve as training data augmentations. The method allows for end-to-end argumentation analysis using a rhetorical tree instead of a word sequence. It is evaluated on the bilingual Microtexts corpus, and the first results on fully-fledged argument parsing for the Russian version of the corpus are reported. The results suggest that argument mining can benefit from multiple variants of discourse structure.