To improve students' critical thinking skills, we first need to evaluate their argumentative texts, i.e., identify argumentative errors. Although identifying such argumentative structures (components, relations, and schemes) and properties (fallacies and debate patterns) is important, it has limitations in terms of effective feedback. Identifying a missing claim or a wrong premise is insufficient to understand how to improve the argumentation properly. Thus, we relate the identification of structure and properties to shallow explanations in the sense that users can still benefit from the output of the models.
Shallow explanations can be difficult to understand, especially for beginners, as they tend to be minimalist and lack guidance. To explain more effectively the errors in an argument, a model should go a step further, hence by providing in-depth explanations, which attempt to identify the argument's implicit components to explain why it is an error in a particular argument. In Figure 2, we implicitly know that hamburgers belong to the American cuisine, as same as the Cobb salad, a healthy garden salad from California. Therefore, if the model is able to reason out this implicit knowledge, it can better explain the invalid generalization.
In this section, you can find relevant works that focus either on argumentative structures (components, relations, and schemes), properties (fallacies and debate patterns) and implicit reasoning abilities of models.
Subsection | Title | Date | Author | Reference |
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Components | Counter-argument generation by attacking weak premises | 2021 | Milad Alshomary, Shahbaz Syed, Arkajit Dhar, Martin Potthast, and Henning Wachsmuth | In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 1816--1827, Online. Association for Computational Linguistics. |
Debate patterns | Have my arguments been replied to? argument pair extraction as machine reading comprehension | 2022 | Jianzhu Bao, Jingyi Sun, Qinglin Zhu, and Ruifeng Xu | In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 29--35, Dublin, Ireland. Association for Computational Linguistics. |
Implicit Knowledge and Reasoning in Arguments | Implicit knowledge in argumentative texts: An annotated corpus | 2020 | Maria Becker, Katharina Korfhage, and Anette Frank | In Proceedings of the 12th Language Resources and Evaluation Conference, pages 2316--2324, Marseille, France. European Language Resources Association. |
Implicit Knowledge and Reasoning in Arguments | Reconstructing implicit knowledge with language models | 2021 | Maria Becker, Siting Liang, and Anette Frank | 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 Computational Linguistics. |
Fallacies | The search for agreement on logical fallacy annotation of an infodemic | 2022 | Claire Bonial, Austin Blodgett, Taylor Hudson, Stephanie M Lukin, Jeffrey Micher, Douglas Summers-Stay, Peter Sutor, and Clare Voss | In Proceedings of the 13th Language Resources and Evaluation Conference, pages 4430--4438, Marseille, France. European Language Resources Association. |
Debate patterns | A model for processing illocutionary structures and argumentation in debates. | 2014 | Kasia Budsziyska, Mathilde Janier, Chris Reed, Patrick Saint-Dizier, Manfred Stede, and Olena Yakorska | In Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), volume 14, pages electronic--medium. European Language Resources Association (ELRA). |
Implicit Knowledge and Reasoning in Arguments | In Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE 2023) | 2023 | Bhavana Dalvi Mishra, Greg Durrett, Peter Jansen, Danilo Neves Ribeiro, and Jason Wei, editors | Association for Computational Linguistics, Toronto, Canada. |
Schemes | Classifying arguments by scheme | 2011 | Vanessa Wei Feng and Graeme Hirst | In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 987--996, Portland, Oregon, USA. Association for Computational Linguistics. |
Fallacies | Fallacious argument classification in political debates | 2022 | Pierpaolo Goffredo, Shohreh Haddadan, Vorakit Vorakitphan, Elena Cabrio, and Serena Villata | In Proceedings of the 31st International Joint Conference on Artificial Intelligence IJCAI 2022, pages 4143--4149. International Joint Conferences on Artificial Intelligence Organization. |
Implicit Knowledge and Reasoning in Arguments | QT30: A corpus of argument and conflict in broadcast debate | 2022 | Annette Hautli-Janisz, Zlata Kikteva, Wassiliki Siskou, Kamila Gorska, Ray Becker, and Chris Reed | In Proceedings of the 13th Language Resources and Evaluation Conference, pages 3291--3300, Marseille, France. European Language Resources Association. |
Implicit Knowledge and Reasoning in Arguments | Strategies for framing argumentative conclusion generation | 2022 | Philipp Heinisch, Anette Frank, Juri Opitz, and Philipp Cimiano | In Proceedings of the 15th International Conference on Natural Language Generation, pages 246--259, Waterville, Maine, USA and virtual meeting. Association for Computational Linguistics. |
Implicit Knowledge and Reasoning in Arguments | Towards reasoning in large language models: A survey | 2023 | Jie Huang and Kevin Chen-Chuan Chang | In Findings of the Association for Computational Linguistics: ACL 2023, pages 1049--1065, Toronto, Canada. Association for Computational Linguistics. |
Fallacies | Logical fallacy detection | 2022 | Zhijing Jin, Abhinav Lalwani, Tejas Vaidhya, Xiaoyu Shen, Yiwen Ding, Zhiheng Lyu, Mrinmaya Sachan, Rada Mihalcea, and Bernhard Schoelkopf | In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 7180--7198, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. |
Components | Extracting implicitly asserted propositions in argumentation | 2020 | Yohan Jo, Jacky Visser, Chris Reed, and Eduard Hovy | In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 24–38, Online. Association for Computational Linguistics. |
Implicit Knowledge and Reasoning in Arguments | Knowledge-enhanced evidence retrieval for counterargument generation. | 2021 | Yohan Jo, Haneul Yoo, JinYeong Bak, Alice Oh, Chris Reed, and Eduard Hovy | In Findings of the Association for Computational Linguistics: EMNLP 2021, Online. Association for Computational Linguistics. |
Debate patterns | The keystone role played by questions in debate | 2022 | Zlata Kikteva, Kamila Gorska, Wassiliki Siskou, Annette Hautli-Janisz, and Chris Reed | In Proceedings of the 3rd Workshop on Computational Approaches to Discourse, pages 54--63, Gyeongju, Republic of Korea and Online. International Conference on Computational Linguistics. |
Relations | LPAttack: A feasible annotation scheme for capturing logic pattern of attacks in arguments | 2022 | Farjana Sultana Mim, Naoya Inoue, Shoichi Naito, Keshav Singh, and Kentaro Inui | In Proceedings of the 13th Language Resources and Evaluation Conference, pages 2446--2459, Marseille, France. European Language Resources Association. |
Fallacies | Automated discovery of logical fallacies in legal argumentation | 2020 | Callistus Ireneous Nakpih and Simone Santini | International Journal of Artificial Intelligence & Applications. |
Implicit Knowledge and Reasoning in Arguments | Finding enthymemes in real-world texts: A feasibility study | 2017 | Olesya Razuvayevskaya and Simone Teufel | Argument Computation. |
Implicit Knowledge and Reasoning in Arguments | Uncovering implicit inferences for improved relational argument mining | 2023 | Ameer Saadat-Yazdi, Jeff Z Pan, and Nadin Kokciyan | In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 2484--2495, Dubrovnik, Croatia. Association for Computational Linguistics. |
Implicit Knowledge and Reasoning in Arguments | IRAC: A domain-specific annotated corpus of implicit reasoning in arguments | 2022 | Keshav Singh, Naoya Inoue, Farjana Sultana Mim, Shoichi Naito, and Kentaro Inui | In Proceedings of the 13th Language Resources and Evaluation Conference, pages 4674--4683, Marseille, France. European Language Resources Association. |
Implicit Knowledge and Reasoning in Arguments | Exploring methodologies for collecting high-quality implicit reasoning in arguments | 2021 | Keshav Singh, Farjana Sultana Mim, Naoya Inoue, Shoichi Naito, and Kentaro Inui | In Proceedings of the 8th Workshop on Argument Mining, pages 57--66, Punta Cana, Dominican Republic. Association for Computational Linguistics. |
Schemes | Applying argumentation schemes for essay scoring | 2014 | Yi Song, Michael Heilman, Beata Beigman Klebanov, and Paul Deane | In Proceedings of the 1st Workshop on Argumentation Mining, pages 69--78, Baltimore, Maryland. Association for Computational Linguistics. |
Components | Identifying argumentative discourse structures in persuasive essays | 2014 | Christian Stab and Iryna Gurevych | In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 46--56, Doha, Qatar. Association for Computational Linguistics. |
Components | Argumentative Zoning: Information Extraction from Scientific Text. | 1999 | Simone Teufel | Ph.D. thesis, University of Edinburgh. |
Schemes | Argumentation schemes. | 2008 | Douglas Walton, Christopher Reed, and Fabrizio Macagno | Cambridge University Press. |
Implicit Knowledge and Reasoning in Arguments | Towards understanding chain-of-thought prompting: An empirical study of what matters | 2023 | Boshi Wang, Sewon Min, Xiang Deng, Jiaming Shen, You Wu, Luke Zettlemoyer, and Huan Sun | In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023), pages 2717--2739, Toronto, Canada. Association for Computational Linguistics. |
Implicit Knowledge and Reasoning in Arguments | Chain-of-thought prompting elicits reasoning in large language models | 2022 | Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou | In Advances in Neural Information Processing Systems, volume 35, pages 24824--24837. Curran Associates, Inc. |
Relations | Leveraging argumentation knowledge graph for interactive argument pair identification | 2021 | Jian Yuan, Zhongyu Wei, Donghua Zhao, Qi Zhang, and Changjian Jiang | In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 2310--2319, Online. Association for Computational Linguistics. |
Fallacies | Case-based reasoning with language models for classification of logical fallacies | 2023 | Sourati Zhivar, Ilievski Filip, Sandlin Hông-Ân, and Mermoud Alain | In Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023) |
Fallacies | Argotario: Computational argumentation meets serious games | 2017 | Ivan Habernal, Raffael Hannemann, Christian Pollak, Christopher Klamm, Patrick Pauli, and Iryna Gurevych | In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 7--12, Copenhagen, Denmark. Association for Computational Linguistics. |
Debate patterns | TYPIC: A corpus of template-based diagnostic comments on argumentation | 2022 | Shoichi Naito, Shintaro Sawada, Chihiro Nakagawa, Naoya Inoue, Kenshi Yamaguchi, Iori Shimizu, Farjana Sultana Mim, Keshav Singh, and Kentaro Inui | In Proceedings of the 13th Language Resources and Evaluation Conference, pages 5916--5928, Marseille, France. European Language Resources Association. |