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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 standalone measures to yield improved correlation and accuracy.
|Title of host publication||Proceedings of ACL 2018, Student Research Workshop|
|Place of Publication||Australia|
|Publisher||Association for Computational Linguistics|
|Publication status||Published - 2018|
|Event||56th Annual Meeting of Association for Computational Linguistics - Melbourne, Australia|
Duration: 15 Jul 2018 → 20 Jul 2018
|Conference||56th Annual Meeting of Association for Computational Linguistics|
|Period||15/07/18 → 20/07/18|
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