Yamen Ajjour, Henning Wachsmuth, Dora Kiesel, Patrick Riehmann, Fan Fan, Giuliano Castiglia, Rosemary Adejoh, Bernd Fröhlich, Benno Stein
| Challenge: | args.me is the first search engine for controversial topics . it ranks pro and con arguments by their relevance to a topic . |
| Approach: | They propose a visualization interface for result exploration that provides an overview of main aspects in a barycentric coordinate system. |
| Outcome: | The proposed search engine is the first dedicated argument search engine on the web. |
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| Challenge: | Argumentative dialogue systems and chat bots require a database of arguments that matches their requirements. |
| Approach: | They propose a dialogue system that presents arguments by virtual avatar and synthetic speech to users and allows them to rate the presented content in four different categories. |
| Outcome: | The proposed system evaluates arguments retrieved by two state-of-the-art argument search engines and a system based on traditional web search. |
Topic Ontologies for Arguments (2023.findings-eacl)
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| Challenge: | Many computational argumentation tasks, such as stance classification, are topic-dependent. |
| Approach: | They map the argumentation landscape using the World Economic Forum, Wikipedia and Debatepedia as sources for argument topics. |
| Outcome: | The argument ontology is the first comprehensive assessment of argument topics in argument corpora. |
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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ArgBench: Benchmarking LLMs on Computational Argumentation Tasks (2026.findings-acl)
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| Challenge: | Argumentation skills are an essential toolkit for large language models (LLMs). |
| Approach: | They propose a benchmark to evaluate the generalizability of five LLM families across 46 computational argumentation tasks. |
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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 . |
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ArgumenText: Searching for Arguments in Heterogeneous Sources (N18-5)
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Christian Stab, Johannes Daxenberger, Chris Stahlhut, Tristan Miller, Benjamin Schiller, Christopher Tauchmann, Steffen Eger, Iryna Gurevych
| Challenge: | Argument mining is a core technology for enabling argument search in large corpora . but current methods fail when applied to heterogeneous texts . despite its obvious applications, argument search has attracted relatively little attention . |
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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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Selective Vision is the Challenge for Visual Reasoning: A Benchmark for Visual Argument Understanding (2024.emnlp-main)
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| Challenge: | Visual arguments rely on images to persuade viewers to do or believe something . |
| Approach: | They propose three tasks for evaluating visual argument understanding . they use visual premises, commonsense premises and reasoning trees to analyze visual arguments . |
| Outcome: | The proposed tasks evaluate visual argument understanding using a dataset of 1,611 images annotated with 5,112 visual premises (with regions), 5,574 commonsense premises, and reasoning trees connecting them into structured arguments. |
Exploring the Potential of Large Language Models in Computational Argumentation (2024.acl-long)
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| Challenge: | Argumentation is an essential tool in various domains, including law, public policy, and artificial intelligence. |
| Approach: | They propose to evaluate LLMs on various computational argumentation tasks . they organize existing tasks into six main categories and standardize the format of 14 datasets . |
| Outcome: | The proposed model performs well on argument mining and argument generation tasks. |
Mining, Assessing, and Improving Arguments in NLP and the Social Sciences (2023.eacl-tutorials)
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| Challenge: | a tutorial on argument quality assessment will focus on what makes an argument good or bad . argument quality is a field encompassing varying tasks on the automated analysis and synthesis of natural language arguments. |
| Approach: | This tutorial will focus on the assessment of argument quality across disciplines . authors will involve participants in annotation studies on the quality assessment . |
| Outcome: | The tutorial will focus on the assessment of argument quality across disciplines . it will involve participants in two annotation studies on the quality assessment and the improvement of quality . |