Summary of Better Than Random: Reliable Nlg Human Evaluation with Constrained Active Sampling, by Jie Ruan et al.
Better than Random: Reliable NLG Human Evaluation with Constrained Active Sampling
by Jie Ruan, Xiao Pu, Mingqi Gao, Xiaojun Wan, Yuesheng Zhu
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Constrained Active Sampling Framework (CASF) is proposed to enhance the reliability of human evaluation in Natural Language Generation (NLG). The framework aims to select representative samples for inter-system ranking, reducing labor and costs while maintaining accuracy. CASF consists of a Learner, Systematic Sampler, and Constrained Controller, which work together to identify the most informative data points. Evaluation results on 137 real NLG setups with 44 human metrics across 16 datasets and 5 tasks demonstrate CASF’s effectiveness, achieving 93.18% top-ranked system recognition accuracy and ranking first or second on 90.91% of human metrics. The overall inter-system ranking Kendall correlation is 0.83. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CASF helps make human evaluation in NLG more reliable by selecting the best samples for comparison. This framework reduces the need for expensive and time-consuming evaluations, making it a valuable tool for researchers. By using CASF, you can get a better idea of which systems perform well across different tasks and datasets. |
