From DQI to C-DQI: Automatic Codebook Generation and Evaluation for Measuring Computational Discourse Quality Index
Starting from the widely accepted discourse quality Index (DQI), we compare top-down (theory-based), bottom-up (data-driven), and hybrid approaches for generating context-adapted schemas.
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Key points
- Effective democratic deliberation is vital for civic engagement and the health of legislative institutions.
- While Large Language Models (LLMs) offer a scalable alternative for both schema induction and discourse annotation, ensuring schema quality and mitigating models' inherent bias remains a clear challenge.
- In this work, we propose a complete protocol for LLM-based codebook generation and evaluation.
- Our experiments on 100 Israeli parliament transcripts reveal that inductive, data-driven schemas achieve higher IMA than top-down methods while maintaining validity, demonstrating their potential for reliable, large-scale analysis of political discourse.
Sources (1)
- [1]From DQI to C-DQI: Automatic Codebook Generation and Evaluation for Measuring Computational Discourse Quality IndexarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 09:45 AM
Starting from the widely accepted discourse quality Index (DQI), we compare top-down (theory-based), bottom-up (data-driven), and hybrid approaches for generating context-adapted schemas.
Effective democratic deliberation is vital for civic engagement and the health of legislative institutions.
Extractive summary: sentences quoted from the sources.
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