Unveiling NLG Human-Evaluation Reproducibility: Lessons Learned and Key Insights from Participating in the ReproNLP Challenge

Watson, Lewis; Gkatzia, Dimitra

Abstract

Human evaluation is crucial for NLG systems as it provides a reliable assessment of the quality, effectiveness, and utility of generated language outputs. However, concerns about the reproducibility of such evaluations have emerged, casting doubt on the reliability and generalisability of reported results. In this paper, we present the findings of a reproducibility study on a data-to-text system, conducted under two conditions: (1) replicating the original setup as closely as possible with evaluators from AMT, and (2) replicating the original human evaluation but this time, utilising evaluators with a background in academia. Our experiments show that there is a loss of statistical significance between the original and reproduction studies, i.e. the human evaluation results are not reproducible. In addition, we found that employing local participants led to more robust results. We finally discuss lessons learned, addressing the challenges and best practices for ensuring reproducibility in NLG human evaluations.

Citation

Watson, L., & Gkatzia, D. (2023, September). Unveiling NLG Human-Evaluation Reproducibility: Lessons Learned and Key Insights from Participating in the ReproNLP Challenge. Presented at 3rd Workshop on Human Evaluation of NLP Systems (HumEval), Varna, Bulgaria

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 3rd Workshop on Human Evaluation of NLP Systems (HumEval)
• Publication Date: 2023
• Deposit Date: Feb 6, 2024
• Publicly Available Date: Feb 6, 2024
• Publisher: Association for Computational Linguistics (ACL)
• Pages: 69-74
• Book Title: Proceedings of the 3rd Workshop on Human Evaluation of NLP Systems
• Public URL: http://researchrepository.napier.ac.uk/Output/3496961
• Publisher URL: https://aclanthology.org/2023.humeval-1.6/