BERTHA: Video Captioning Evaluation Via Transfer-Learned Human Assessment

Luis Lebron, Yvette Graham, Kevin McGuinness, Konstantinos Kouramas, Noel E. O’Connor

Abstract
Evaluating video captioning systems is a challenging task as there are multiple factors to consider; for instance: the fluency of the caption, multiple actions happening in a single scene, and the human bias of what is considered important. Most metrics try to measure how similar the system generated captions are to a single or a set of human-annotated captions. This paper presents a new method based on a deep learning model to evaluate these systems. The model is based on BERT, which is a language model that has been shown to work well in multiple NLP tasks. The aim is for the model to learn to perform an evaluation similar to that of a human. To do so, we use a dataset that contains human evaluations of system generated captions. The dataset consists of the human judgments of the captions produces by the system participating in various years of the TRECVid video to text task. BERTHA obtain favourable results, outperforming the commonly used metrics in some setups.
Anthology ID:
2022.lrec-1.168
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1566–1575
Language:
URL:
https://aclanthology.org/2022.lrec-1.168
DOI:
Bibkey:
Cite (ACL):
Luis Lebron, Yvette Graham, Kevin McGuinness, Konstantinos Kouramas, and Noel E. O’Connor. 2022. BERTHA: Video Captioning Evaluation Via Transfer-Learned Human Assessment. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1566–1575, Marseille, France. European Language Resources Association.
Cite (Informal):
BERTHA: Video Captioning Evaluation Via Transfer-Learned Human Assessment (Lebron et al., LREC 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl-24-ws-corrections/2022.lrec-1.168.pdf
Code
 LLebronC/TRECvid-VTT_HA
PDF Search Code