Image captioning studies heavily rely on automatic evaluation metrics such as BLEU and METEOR. However, such n-gram-based metrics have been shown to correlate poorly with human evaluation, leading to the proposal of alternative metrics such as SPICE for English; however, no equivalent metrics have been established for other languages. Therefore, in this study, we propose an automatic evaluation metric called JaSPICE, which evaluates Japanese captions based on scene graphs. The proposed method generates a scene graph from dependencies and the predicate-argument structure, and extends the graph using synonyms. We conducted experiments employing 10 image captioning models trained on STAIR Captions and PFN-PIC and constructed the Shichimi dataset, which contains 103,170 human evaluations. The results showed that our metric outperformed the baseline metrics for the correlation coefficient with the human evaluation.

翻译：图像描述研究高度依赖BLEU和METEOR等自动评估指标。然而，这类基于n-gram的指标已被证明与人工评估的相关性较差，因此针对英语提出了SPICE等替代指标，但其他语言尚未建立相应的评估指标。为此，本研究提出一种名为JaSPICE的自动评估指标，该指标基于场景图对日语描述进行评估。所提出的方法从依存关系和谓词-参数结构中生成场景图，并利用同义词对图进行扩展。我们使用在STAIR Captions和PFN-PIC上训练的10个图像描述模型进行实验，构建了包含103,170个人工评估的Shichimi数据集。结果表明，我们提出的指标在与人工评估的相关系数上优于基线指标。