| Challenge: | Existing models for understanding debate dialog ignore relationships between different topics and focus on textual content and user interaction. |
| Approach: | They propose to view this task as a representation learning problem and embed the text and authors jointly based on their interactions. |
| Outcome: | The proposed model can achieve significantly better results compared to competing models. |
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| Challenge: | Existing research on monological argumentation covers claims generation, argument structure prediction, and essay scoring. |
| Approach: | They propose to identify argument pairs from two posts with opposite stances to a certain topic. |
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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. |
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Modeling Online Discourse with Coupled Distributed Topics (D18-1)
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Can We Identify Stance without Target Arguments? A Study for Rumour Stance Classification (2024.lrec-main)
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| Challenge: | Existing target-aware models underperform in cases where the context of the target is crucial. |
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Dynamic Stance: Modeling Discussions by Labeling the Interactions (2023.findings-emnlp)
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| Challenge: | Stance detection is a popular task that has been modeled as a static task, but its limitations are strong topic-dependent. |
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| Challenge: | a vision of computational construction of discourse networks from newspaper reports is essential for understanding democratic political decision making. |
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CoFE: A New Dataset of Intra-Multilingual Multi-target Stance Classification from an Online European Participatory Democracy Platform (2022.aacl-short)
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| Challenge: | Stance Recognition is a useful tool for many real-life applications, from misinformation detection to poll verification. |
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STANCY: Stance Classification Based on Consistency Cues (D19-1)
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Exploring the Role of Argument Structure in Online Debate Persuasion (2020.emnlp-main)
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Unveiling the Power of Argument Arrangement in Online Persuasive Discussions (2023.findings-emnlp)
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