| Challenge: | Existing work on the automatic generation of natural language counter-arguments does not address the relation to the conclusion, possibly because many arguments leave their conclusion implicit. |
| Approach: | They propose a multitask approach that jointly learns to generate both the conclusion and the counter of an input argument. |
| Outcome: | The proposed approach generates more relevant and stance-adhering counters than strong baselines. |
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| Challenge: | Existing approaches focus on generating single claims, but there are limitations. |
| Approach: | They propose to use a triplet neural network to infer a conclusion's target from premises' targets and a neural network for a new target. |
| Outcome: | The proposed approach outperforms baselines on two domains. |
Argument Generation with Retrieval, Planning, and Realization (P19-1)
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| Challenge: | a novel argument generation framework is used to generate counter-arguments . CANDELA uses a text planning decoder to retrieve arguments of different perspectives . |
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LLM DEBATE OPPONENT : Counter-argument Generation focusing on Implicit and Critical Premises (2025.naacl-srw)
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| Challenge: | Recent advances in Large Language Models (LLMs) show promise in automating counter-argument generation. |
| Approach: | They compare multi-step and one-step generation methods for counter-arguments across 100 debate topics. |
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Neural Argument Generation Augmented with Externally Retrieved Evidence (P18-1)
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| Challenge: | Existing methods for generating arguments are limited to retrieval-based methods. |
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Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation (2023.emnlp-main)
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Jiayu Lin, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Zhongyu Wei
| Challenge: | Existing work describes paragraph-level counter-argument generation task as paragraph-based . however, sentence-level generation can be quite different due to its unique constraints and brevity-focused challenges. |
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Counter-Argument Generation by Attacking Weak Premises (2021.findings-acl)
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| Challenge: | a recent work explores the generation of counter-arguments by undermining one of its premises . identifying the argument's weak premises is key to effective countering, we hypothesize . |
| Approach: | They propose a pipeline approach that first assesses the argument's weak premises and generates a counter-argument undermining the weakest among them. |
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A Dataset of General-Purpose Rebuttal (D19-1)
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Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim
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Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation (2024.findings-acl)
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Hao Li, Yuping Wu, Viktor Schlegel, Riza Batista-Navarro, Tharindu Madusanka, Iqra Zahid, Jiayan Zeng, Xiaochi Wang, Xinran He, Yizhi Li, Goran Nenadic
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Dynamic Knowledge Integration for Evidence-Driven Counter-Argument Generation with Large Language Models (2025.findings-acl)
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| Challenge: | Argumentation in natural language processing (NLP) is becoming an indispensable tool in many application domains such as public policy, law, medicine, and education. |
| Approach: | They propose a reconstructed dataset of argument and counter-argument pairs . they propose integrating dynamic external knowledge from the web to improve counter-arguments . |
| Outcome: | The proposed method shows stronger correlation with human judgments compared to reference-based metrics. |
Determining Relative Argument Specificity and Stance for Complex Argumentative Structures (P19-1)
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| Challenge: | Existing work on claim specificity and stance has been limited to shallow arguments . a system that can determine the stance of claims employed in argumentation is not sufficient . |
| Approach: | They propose to use a dataset of manually curated argument trees to study claim specificity and stance in argumentation. |
| Outcome: | The proposed dataset consists of manually curated argument trees for 741 controversial topics covering 95,312 unique claims. |