Learning-based Composite Metrics for Improved Caption Evaluation
Naeha Sharif, Lyndon White, Mohammed Bennamoun, Syed Afaq Ali Shah
Abstract
The evaluation of image caption quality is a challenging task, which requires the assessment of two main aspects in a caption: adequacy and fluency. These quality aspects can be judged using a combination of several linguistic features. However, most of the current image captioning metrics focus only on specific linguistic facets, such as the lexical or semantic, and fail to meet a satisfactory level of correlation with human judgements at the sentence-level. We propose a learning-based framework to incorporate the scores of a set of lexical and semantic metrics as features, to capture the adequacy and fluency of captions at different linguistic levels. Our experimental results demonstrate that composite metrics draw upon the strengths of stand-alone measures to yield improved correlation and accuracy.- Anthology ID:
- P18-3003
- Volume:
- Proceedings of ACL 2018, Student Research Workshop
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 14–20
- Language:
- URL:
- https://aclanthology.org/P18-3003
- DOI:
- 10.18653/v1/P18-3003
- Cite (ACL):
- Naeha Sharif, Lyndon White, Mohammed Bennamoun, and Syed Afaq Ali Shah. 2018. Learning-based Composite Metrics for Improved Caption Evaluation. In Proceedings of ACL 2018, Student Research Workshop, pages 14–20, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Learning-based Composite Metrics for Improved Caption Evaluation (Sharif et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/P18-3003.pdf