Research map / Label-efficient and robust learning / Recognition and long-tail
Never Retreat, Never Retract: Argumentation Analysis for Political Speeches
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use two cascaded binary classifiers instead of one 3-class classifier (ablation) | SVM relation classifier · Nixon-Kennedy 1960 | precision: 0.57 vs 0.72 (end-to-end accuracy) | Helped |
| evaluate on Step 1 output instead of gold related pairs | Step 2 classifier · Nixon-Kennedy 1960 | accuracy: 0.82 -> 0.72 | Hurt |
| minimum wage topic versus other topics | Step 2 classifier · Nixon-Kennedy 1960 minimum wage | accuracy: 0.94 | Helped |
| pairs by same speaker versus different speakers | Step 2 classifier · Nixon-Kennedy 1960 | accuracy: 0.86 vs 0.79 | Helped |
| SVM pipeline versus random baseline on gold pairs | random baseline · Nixon-Kennedy 1960 Step 2 gold | accuracy: 0.70 -> 0.82 | Helped |
| SVM pipeline versus random baseline on end-to-end | random baseline · Nixon-Kennedy 1960 Step 2 end-to-end | accuracy: 0.51 -> 0.72 | Helped |
Closest papers by meaning
- Argument Mining from Speech: Detecting Claims in Political Debates (AAAI 2016 · similarity 0.89)
- DISPUTool 2.0: A Modular Architecture for Multi-Layer Argumentative Analysis of Political Debates (AAAI 2023 · similarity 0.89)
- OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset (NeurIPS 2024 · similarity 0.87)
- Corpus Wide Argument Mining—A Working Solution (AAAI 2020 · similarity 0.86)
- The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions (ICLR 2025 · similarity 0.82)
- Debating with More Persuasive LLMs Leads to More Truthful Answers (ICML 2024 · similarity 0.80)
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