Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
102 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

End-to-End Argument Mining over Varying Rhetorical Structures (2401.11218v1)

Published 20 Jan 2024 in cs.CL

Abstract: 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.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (35)
  1. Pablo Accuosto and Horacio Saggion. 2019. Transferring knowledge from discourse to arguments: A case study with scientific abstracts. In Proceedings of the 6th Workshop on Argument Mining, pages 41–51, Florence, Italy. Association for Computational Linguistics.
  2. The change that matters in discourse parsing: Estimating the impact of domain shift on parser error. In Findings of the Association for Computational Linguistics: ACL 2022, pages 824–845, Dublin, Ireland. Association for Computational Linguistics.
  3. Moshe Azar. 1999. Argumentative text as rhetorical structure: An application of rhetorical structure theory. Argumentation, 13(1):97–114.
  4. Class-based n-gram models of natural language. Computational linguistics, 18(4):467–480.
  5. Building a discourse-tagged corpus in the framework of Rhetorical Structure Theory. In Proceedings of the Second SIGdial Workshop on Discourse and Dialogue.
  6. RST discourse parser for Russian: an experimental study of deep learning models. In International Conference on Analysis of Images, Social Networks and Texts, pages 105–119. Springer.
  7. Marta R Costa-jussà et al. 2022. No language left behind: Scaling human-centered machine translation. arXiv preprint arXiv:2207.04672.
  8. Iria Da Cunha and Mikel Iruskieta. 2010. Comparing rhetorical structures in different languages: The influence of translation strategies. Discourse Studies, 12(5):563–598.
  9. Timothy Dozat and Christopher D Manning. 2016. Deep biaffine attention for neural dependency parsing. arXiv preprint arXiv:1611.01734.
  10. Vanessa Wei Feng and Graeme Hirst. 2014. A linear-time bottom-up discourse parser with constraints and post-editing. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 511–521, Baltimore, Maryland. Association for Computational Linguistics.
  11. Irina Fishcheva and Evgeny Kotelnikov. 2019. Cross-lingual argumentation mining for Russian texts. In International Conference on Analysis of Images, Social Networks and Texts, pages 134–144.
  12. Detecting logical argumentation in text via communicative discourse tree. Journal of Experimental & Theoretical Artificial Intelligence, 30(5):637–663.
  13. AllenNLP: A deep semantic natural language processing platform. In Proceedings of Workshop for NLP Open Source Software (NLP-OSS), pages 1–6, Melbourne, Australia. Association for Computational Linguistics.
  14. Nancy L. Green. 2010. Representation of argumentation in text with rhetorical structure theory. Argumentation, 24(2):181–196.
  15. Nancy L. Green. 2015. Annotating evidence-based argumentation in biomedical text. In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 922–929.
  16. Debertav3: Improving deberta using electra-style pre-training with gradient-disentangled embedding sharing. arXiv preprint arXiv:2111.09543.
  17. The utility of discourse parsing features for predicting argumentation structure. In Proceedings of the 6th Workshop on Argument Mining, pages 98–103, Florence, Italy. Association for Computational Linguistics.
  18. Matthew Honnibal et al. 2020. spaCy: Industrial-strength natural language processing in Python.
  19. A qualitative comparison method for rhetorical structures: identifying different discourse structures in multilingual corpora. Language resources and evaluation, 49(2):263–309.
  20. Yang Janet Liu and Amir Zeldes. 2023. Why can’t discourse parsing generalize? a thorough investigation of the impact of data diversity. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3112–3130, Dubrovnik, Croatia. Association for Computational Linguistics.
  21. DMRST: A joint framework for document-level multilingual RST discourse segmentation and parsing. In Proceedings of the 2nd Workshop on Computational Approaches to Discourse, pages 154–164, Punta Cana, Dominican Republic and Online. Association for Computational Linguistics.
  22. William C Mann and Sandra A Thompson. 1988. Rhetorical structure theory: Toward a functional theory of text organization. Text-interdisciplinary Journal for the Study of Discourse, 8(3):243–281.
  23. How much progress have we made on RST discourse parsing? a replication study of recent results on the RST-DT. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1319–1324, Copenhagen, Denmark. Association for Computational Linguistics.
  24. Andreas Peldszus and Manfred Stede. 2015a. An annotated corpus of argumentative microtexts. In Argumentation and Reasoned Action: Proceedings of the 1st European Conference on Argumentation, Lisbon, volume 2, pages 801–815.
  25. Andreas Peldszus and Manfred Stede. 2015b. Joint prediction in MST-style discourse parsing for argumentation mining. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 938–948, Lisbon, Portugal. Association for Computational Linguistics.
  26. Andreas Peldszus and Manfred Stede. 2016. Rhetorical structure and argumentation structure in monologue text. In Proceedings of the Third Workshop on Argument Mining (ArgMining2016), pages 103–112, Berlin, Germany. Association for Computational Linguistics.
  27. More or less controlled elicitation of argumentative text: Enlarging a microtext corpus via crowdsourcing. In Proceedings of the 5th Workshop on Argument Mining, pages 155–163, Brussels, Belgium. Association for Computational Linguistics.
  28. Parallel discourse annotations on a corpus of short texts. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 1051–1058, Portorož, Slovenia. European Language Resources Association (ELRA).
  29. St Toulmin. 1958. The uses of argument. Cambridge University Press.
  30. Maria Paz Garcia Villalba and Patrick Saint-Dizier. 2012. Some facets of argument mining for opinion analysis. COMMA, 245:23–34.
  31. Douglas Walton. 2011. How to refute an argument using artificial intelligence. Studies in Logic, Grammar and Rhetoric, 23(36):123–154.
  32. RST-tace a tool for automatic comparison and evaluation of RST trees. In Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019, pages 88–96, Minneapolis, MN. Association for Computational Linguistics.
  33. Amir Zeldes. 2016. rstWeb - a browser-based annotation interface for Rhetorical Structure Theory and discourse relations. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pages 1–5, San Diego, California. Association for Computational Linguistics.
  34. Amir Zeldes. 2017. The GUM corpus: Creating multilayer resources in the classroom. Language Resources and Evaluation, 51(3):581–612.
  35. Adversarial learning for discourse rhetorical structure parsing. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3946–3957, Online. Association for Computational Linguistics.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (1)
  1. Elena Chistova (3 papers)
Citations (3)
View on Semantic Scholar
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets