Elamur Research⌕
☰
home / papers / 2026 / Is There Anything More Deceptive than an Obvious Fact? Investigating Implicitness in User-Generated Argumentative Text

Is There Anything More Deceptive than an Obvious Fact? Investigating Implicitness in User-Generated Argumentative Text

Summary

People rarely spell out every part of an argument. They leave out premises, conclusions or background knowledge they expect the audience to share. Humans fill these gaps easily, but NLP systems struggle with such incomplete arguments (enthymemes). Earlier work mostly studied them on short, tidy texts or in constrained setups. Sviridova, Cabrio and Villata propose a pragmatics-based typology that divides implicitness into three kinds: implicature, ambiguity and presupposition. They use it to manually annotate two resources. The first is 78 complete dialogues from the Change My View forum on Reddit, which were already marked with claims and premises. The second is 112 short texts from the Microtext corpus, used as a controlled comparison. On these data they build baselines for enthymeme detection and for classifying the implicitness type, comparing encoder-only models with large language models. The experiments show that modeling implicit reasoning is still hard, especially in long, loosely structured conversations. The work is the first to approach enthymeme detection without limits on text length. It also provides an openly released, type-labelled corpus that can support reconstruction of missing content adapted to each type.

Key points

  • The authors present the typology as the first fine-grained classification of implicitness in argumentation, with three classes: implicature, ambiguity and presupposition.
  • The data are 78 full-length Change My View discussions (Tan et al., 2016), preprocessed into one-poster-vs-one-respondent dialogues and stored as XML with claim/premise markup. The authors describe this as the largest public set of real-world enthymemes labelled with implicitness types.
  • The comparison set is 112 Microtext texts (Peldszus and Stede, 2013), each 3–5 sentences long and translated from German into English. It is used to study how text length and structural complexity affect both annotation and automatic analysis.
  • Baselines are reported for two tasks, first spotting enthymemes and second assigning a detailed implicitness type, with both encoder-only models and LLMs evaluated on each. The exact scores appear in the results section, which is missing from the provided extract and is therefore not reproduced here.
  • The corpora, annotation guidelines and code are released on GitHub (ekaterinasviridova4/Investigating-implicitness-in-user-generated-argumentative-text). The paper appeared in the LREC 2026 proceedings, pp. 8303–8316.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/48pa29hp9qoy

@inproceedings{sviridova2026is,
  title = {{Is There Anything More Deceptive than an Obvious Fact? Investigating Implicitness in User-Generated Argumentative Text}},
  author = {Sviridova, Ekaterina and Cabrio, Elena and Villata, Serena},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/48pa29hp9qoy},
  url = {https://doi.org/10.63317/48pa29hp9qoy}
}

References

41 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Josh Achiam, Steven Adler, and OpenAI. 2024. Gpt-4 technical report. ArXiv:2303.08774 [cs.CL]. find our card
  2. Milad Alshomary, Shahbaz Syed, Martin Potthast, and Henning Wachsmuth. 2020. Target inference in argument conclusion generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4334–4345, Online. Association for Computational Linguistics. find
  3. Roy Bar-Haim, Indrajit Bhattacharya, Francesco Dinuzzo, Amrita Saha, and Noam Slonim. 2017. Stance classification of context-dependent claims. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 251–261, Valenci… find
  4. Maria Becker, Katharina Korfhage, and Anette Frank. 2020. Implicit knowledge in argumentative texts: An annotated corpus. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 2316–2324, Marseille, France. European Language Resources Association. find
  5. Maria Becker, Siting Liang, and Anette Frank. 2021. Reconstructing implicit knowledge with language models. In Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 11–24, Online. Association for Computationa… find
  6. Maria Becker, Michael Staniek, Vivi Nastase, and Anette Frank. 2017. Enriching argumentative texts with implicit knowledge. In Natural Language Processing and Information Systems - 22nd International Conference on Applications of Natural Language to Information Systems, NLDB 2017, Proceedings, volum… find
  7. Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020. Longformer: The long-document transformer. find
  8. Filip Boltužić and Jan Šnajder. 2016. Fill the gap! analyzing implicit premises between claims from online debates. In Proceedings of the Third Workshop on Argument Mining, pages 124–133, Berlin, Germany. Association for Computational Linguistics. find
  9. Tuhin Chakrabarty, Aadit Trivedi, and Smaranda Muresan. 2021. Implicit premise generation with discourse-aware commonsense knowledge models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6247–6252, Online and Punta Cana, Dominican Republic. Associat… find
  10. HongSeok Choi and Hyunju Lee. 2018. GIST at SemEval-2018 task 12: A network transferring inference knowledge to argument reasoning comprehension task. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 773–777, New Orleans, Louisiana. Association for Computational Lingui… find
  11. Zvonimir Delas, Brian Plüss, and Ramon Ruiz-Dolz. 2024. An argumentation scheme-based framework for automatic reconstruction of natural language enthymemes. In COMMA, Frontiers in Artificial Intelligence and Applications, pages 61– 72. find
  12. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,… find
  13. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al. 2024. The llama 3 herd of models. ArXiv:2407.21783 [cs.AI]. find
  14. Paul Grice. 1975. Logic and conversation. Syntax and Semantics, 3: Speech Acts:41–58. find
  15. Ivan Habernal, Daniel Faber, Nicola Recchia, Sebastian Bretthauer, Iryna Gurevych, Indra Spiecker genannt Döhmann, and Christoph Burchard. 2023. Mining legal arguments in court decisions. Artificial Intelligence and Law, 32(3):1–38. find
  16. Ivan Habernal, Henning Wachsmuth, Iryna Gurevych, and Benno Stein. 2018. The argument reasoning comprehension task: Identification and reconstruction of implicit warrants. In Proceedings of the 2018 Conference of the NAACL, Volume 1 (Long Papers), pages 1930–1940, New Orleans, Louisiana. Association… find our card
  17. Christopher Hidey, Elena Musi, Alyssa Hwang, Smaranda Muresan, and Kathy McKeown. 2017. Analyzing the semantic types of claims and premises in an online persuasive forum. In Proceedings of the 4th Workshop on Argument Mining, pages 11–21, Copenhagen, Denmark. Association for Computational Linguistic… find
  18. Ioana Hulpus, Jonathan Kobbe, Christian Meilicke, Heiner Stuckenschmidt, Maria Becker, Juri Opitz, Vivi Nastase, and Anette Frank. 2019. Towards explaining natural language arguments with background knowledge. In PRO-FILES/SEMEX@ISWC. find
  19. Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023. Mistral 7b. arXiv preprint arXiv:2310.06825. find
  20. John Lawrence and Chris Reed. 2019. Argument mining: A survey. Computational Linguistics, 45(4):765–818. find our card
  21. Stephen C. Levinson. 2000. Presumptive Meanings: The Theory of Generalized Conversational Implicature. Language, Speech, And Communication. Bradford Books. find
  22. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pre-training approach. find
  23. Edoardo Lombardi Vallauri. 2016. The “exaptation” of linguistic implicit strategies. SpringerPlus, 5. find
  24. Edoardo Lombardi Vallauri, Federica Cominetti, and Viviana Masia. 2022. The persuasive and manipulative power of implicit communication. Journal of Pragmatics, 197:1–7. find
  25. Debjit Paul, Juri Opitz, Maria Becker, Jonathan Kobbe, Graeme Hirst, and Anette Frank. 2020. Argumentative relation classification with background knowledge. In COMMA. find
  26. Andreas Peldszus and Manfred Stede. 2013. From argument diagrams to argumentation mining in texts: A survey. International Journal of Cognitive Informatics and Natural Intelligence, 7:1–31. find
  27. Isaac Persing and Vincent Ng. 2020. Unsupervised argumentation mining in student essays. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6795–6803, Marseille, France. European Language Resources Association. find
  28. Pavithra Rajendran, Danushka Bollegala, and Simon Parsons. 2016. Contextual stance classification of opinions: A step towards enthymeme reconstruction in online reviews. In Proceedings of the Third Workshop on Argument Mining, pages 31–39, Berlin, Germany. Association for Computational Linguistics. find
  29. Olesya Razuvayevskaya and Simone Teufel. 2017. Finding enthymemes in real-world texts: A feasibility study. Argument Computation, 8:1–17. find
  30. Ameer Saadat-Yazdi, Jeff Z. Pan, and Nadin Kokciyan. 2023. Uncovering implicit inferences for improved relational argument mining. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 2484–2495, Dubrovnik, Croatia. Association for Comp… find
  31. Keshav Singh, Paul Reisert, Naoya Inoue, Pride Kavumba, and Kentaro Inui. 2019. Improving evidence detection by leveraging warrants. In Proceedings of the Second Workshop on Fact Extraction and VERification, pages 57–62, Hong Kong, China. find
  32. Dan Sperber and Deirdre Wilson. 1995. Relevance: Communication and Cognition. Blackwell Publishers. find
  33. Christian Stab and Iryna Gurevych. 2014. Identifying argumentative discourse structures in persuasive essays. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pages 46–56, Doha, Qatar. find our card
  34. Christian Stab and Iryna Gurevych. 2017. Parsing argumentation structures in persuasive essays. Computational Linguistics, 43(3):619–659. find our card
  35. Maja Stahl, Nick Düsterhus, Mei-Hua Chen, and Henning Wachsmuth. 2023. Mind the gap: Automated corpus creation for enthymeme detection and reconstruction in learner arguments. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4703–4717, Singapore. Association for Comput… find
  36. Ekaterina Sviridova, Anar Yeginbergen, Ainara Estarrona, Elena Cabrio, Serena Villata, and Rodrigo Agerri. 2024. CasiMedicos-arg: A medical question answering dataset annotated with explanatory argumentative structures. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language P… find
  37. Chenhao Tan, Vlad Niculae, Cristian Danescu-Niculescu-Mizil, and Lillian Lee. 2016. Winning arguments: Interaction dynamics and persuasion strategies in good-faith online discussions. Proceedings of the 25th International Conference on World Wide Web. find
  38. Douglas Walton, Chris Reed, and Fabrizio Macagno. 2008. Argumentation Schemes. Cambridge University Press. find
  39. Lu Wang and Wang Ling. 2016. Neural network-based abstract generation for opinions and arguments. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 47–57, San Diego, California. ACL. find
  40. Michael Wojatzki and Torsten Zesch. 2016. Stance-based argument mining - modeling implicit argumentation using stance. In Conference on Natural Language Processing. find
  41. A. Best Prompts for LLMs in Zero-shot and Fine-tuning Settings Prompt 1 find

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/sviridova-2026-is-there-anything-more-deceptive/