Automatic Metrics in Natural Language Generation: A survey of Current Evaluation Practices

Schmidtova, Patricia; Mahamood, Saad; Balloccu, Simone; Dusek, Ondrej; Gatt, Albert; Gkatzia, Dimitra; Howcroft, David M.; Platek, Ondrej; Sivaprasad, Adarsa

Abstract

Automatic metrics are extensively used to evaluate Natural Language Processing systems. However, there has been increasing focus on how the are used and reported by practitioners within the field. In this paper, we have conducted a survey on the use of automatic metrics, focusing particularly on natural language generation tasks. We inspect which metrics are used as well as why they are chosen and how their use is reported. Our findings from this survey reveal significant shortcomings, including inappropriate metric usage, lack of implementation details and missing correlations with human judgements. We conclude with recommendations that we believe authors should follow to enable more rigour within the field.

Citation

Schmidtova, P., Mahamood, S., Balloccu, S., Dusek, O., Gatt, A., Gkatzia, D., Howcroft, D. M., Platek, O., & Sivaprasad, A. (2024, September). Automatic Metrics in Natural Language Generation: A survey of Current Evaluation Practices. Presented at INLG 2024, Tokyo, Japan

• Presentation Conference Type: Conference Paper (published)
• Conference Name: INLG 2024
• Start Date: Sep 23, 2024
• End Date: Sep 27, 2024
• Acceptance Date: Jul 15, 2024
• Online Publication Date: Oct 1, 2024
• Publication Date: 2024
• Deposit Date: Jul 16, 2024
• Publicly Available Date: Oct 3, 2024
• Publisher: Association for Computational Linguistics (ACL)
• Peer Reviewed: Peer Reviewed
• Pages: 557–583
• Book Title: Proceedings of the 17th International Natural Language Generation Conference
• ISBN: 9798891761223
• Publisher URL: https://aclanthology.org/2024.inlg-main.44
• External URL: https://inlg2024.github.io/