Better than Random: Reliable NLG Human Evaluation with Constrained Active Sampling

TL;DR

This paper introduces CASF, a framework that improves the reliability of human evaluations in NLG by selecting representative samples, achieving high accuracy in system ranking with less human effort.

Contribution

The paper presents a novel Constrained Active Sampling Framework (CASF) that enhances the reliability of human evaluation for NLG by optimizing sample selection.

Findings

CASF achieves 93.18% top-ranked system recognition accuracy.

Ranks first or second on 90.91% of metrics.

Demonstrates robustness across multiple datasets and tasks.

Abstract

Human evaluation is viewed as a reliable evaluation method for NLG which is expensive and time-consuming. To save labor and costs, researchers usually perform human evaluation on a small subset of data sampled from the whole dataset in practice. However, different selection subsets will lead to different rankings of the systems. To give a more correct inter-system ranking and make the gold standard human evaluation more reliable, we propose a Constrained Active Sampling Framework (CASF) for reliable human judgment. CASF operates through a Learner, a Systematic Sampler and a Constrained Controller to select representative samples for getting a more correct inter-system ranking.Experiment results on 137 real NLG evaluation setups with 44 human evaluation metrics across 16 datasets and 5 NLG tasks demonstrate CASF receives 93.18% top-ranked system recognition accuracy and ranks first or ranks second on 90.91% of the human metrics with 0.83 overall inter-system ranking Kendall correlation.Code and data are publicly available online.