BanglaRhet: Benchmarking Classical and Transformer Models for Rhetorical and Persuasion Detection in Bangla Political Speech

Key points

• Political discourse often uses rhetorical and persuasive language to frame narratives, influence public opinion, and mobilize audiences.1
• This paper presents a benchmark study of transformer-based models for detecting rhetorical form and persuasive intent in Bangla political discourse.1
• Using BanglaRhet, a manually annotated corpus of 30,289 Bangla political speech segments collected from publicly available political news sources, we formulate two supervised single-label classification tasks: rhetorical technique detection (contrast, repetition, exaggeration, metaphor, rhetorical questions) and persuasion technique detection (blame assignment, call to action, unity call, moral, emotional, and logical appeals).1
• The results provide initial benchmark baselines for Bangla rhetorical and persuasion-aware political discourse analysis and highlight the need for context-aware and multi-label modeling.1

Sources (1)

• [1]BanglaRhet: Benchmarking Classical and Transformer Models for Rhetorical and Persuasion Detection in Bangla Political Speech
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 05:22 AM

  While Bangla natural language processing has made progress in sentiment analysis and opinion mining, systematic benchmarking of transformer models for fine-grained rhetorical and persuasion technique detection in Bangla political speech remains largely underexplored.

  Political discourse often uses rhetorical and persuasive language to frame narratives, influence public opinion, and mobilize audiences.

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