End-to-End Argument Mining over Varying Rhetorical Structures


Chistova E.


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.

External links

DOI: 10.18653/v1/2023.findings-acl.209

Download PDF from the ACL aritcle: https://aclanthology.org/2023.findings-acl.209/

At the ACL 2023 conference website: https://virtual2023.aclweb.org/paper_P4037.html

Download code and data at GitHub: https://github.com/tchewik/e2e-microtexts

Reference link

Elena Chistova. 2023. End-to-End Argument Mining over Varying Rhetorical Structures // In Findings of the Association for Computational Linguistics: ACL 2023, pages 3376–3391, Toronto, Canada. Association for Computational Linguistics.