| Challenge: | Prior work in NLP studies focus on argument quality and making counterarguments toward the main claim, without investigating what parts of an argument are attackable for successful persuasion. |
| Approach: | They propose to use machine learning to find attackable sentences in online arguments by analyzing driving reasons for attacks and identifying relevant characteristics of sentences. |
| Outcome: | The proposed model can detect attackable sentences significantly better than baselines and comparably well to laypeople. |
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| Challenge: | Existing work focused on detecting claims within a small set of documents . however, pinpointing relevant claims within massive unstructured corpora, received little attention. |
| Approach: | They propose to use a weak signal to develop a query for claim–sentence detection using a large text corpus. |
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Extracting Implicitly Asserted Propositions in Argumentation (2020.emnlp-main)
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| Challenge: | Argumentation is a rhetorical device that asserts propositions implicitly, but few studies have examined the issue. |
| Approach: | They propose a computational method for extracting propositions that are implicitly asserted in questions, reported speech, and imperatives in argumentation. |
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Limited Generalizability in Argument Mining: State-Of-The-Art Models Learn Datasets, Not Arguments (2025.acl-long)
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| Challenge: | Identifying arguments is a prerequisite for various tasks in automated discourse analysis. |
| Approach: | They evaluate four BERT-like transformers on 17 English sentence-level datasets . they find that they tend to rely on lexical shortcuts tied to content words . |
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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. |
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A Corpus for Modeling User and Language Effects in Argumentation on Online Debating (P19-1)
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| Challenge: | Existing argumentation datasets have allowed only limited assessment of "user" traits because information on background of users is generally unavailable. |
| Approach: | They present a dataset of 78,376 debates generated over a 10-year period along with surprisingly comprehensive participant profiles. |
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Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation (N18-1)
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| Challenge: | Existing research lacks solid empirical investigation of typology of ad hominem arguments and their potential causes. |
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A Two-Step Approach for Implicit Event Argument Detection (2020.acl-main)
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| Challenge: | et al., 2015) only consider local arguments in the same sentence of the event trigger. |
| Approach: | They propose to decompose the implicit event argument detection task into two sub-problems . they propose to use argument head-word detection and head-to-span expansion to reduce the number of candidates. |
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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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Can Language Models Recognize Convincing Arguments? (2024.findings-emnlp)
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| Challenge: | Existing studies have found that large language models can generate persuasive content without engaging in human experimentation. |
| Approach: | They extend a dataset with debates, votes, and user traits to measure LLMs' ability to distinguish between strong and weak arguments, predict stances based on beliefs and demographic characteristics, and determine appeal of argument to individual based upon their traits. |
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Learning Strategies for Robust Argument Mining: An Analysis of Variations in Language and Domain (2024.lrec-main)
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| Challenge: | Argument mining is a complex process that requires a large amount of resources and time. |
| Approach: | They propose to analyze arguments in three different languages and domains to understand their robustness to natural language variations. |
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