Semantic embeddings have advanced the state of the art for countless natural language processing tasks, and various extensions to multimodal domains, such as visual-semantic embeddings, have been proposed. While the power of visual-semantic embeddings comes from the distillation and enrichment of information through machine learning, their inner workings are poorly understood and there is a shortage of analysis tools. To address this problem, we generalize the notion of probing tasks to the visual-semantic case. To this end, we (i) discuss the formalization of probing tasks for embeddings of image-caption pairs, (ii) define three concrete probing tasks within our general framework, (iii) train classifiers to probe for those properties, and (iv) compare various state-of-the-art embeddings under the lens of the proposed probing tasks. Our experiments reveal an up to 12% increase in accuracy on visual-semantic embeddings compared to the corresponding unimodal embeddings, which suggest that the text and image dimensions represented in the former do complement each other.

本篇论文提出了一种探测任务的方法，通过训练分类器来比较各种最新的文本-图像语义嵌入，揭示了语义嵌入中存在的问题并提出了问题解决方案。实验结果表明，视觉-语义嵌入的识别准确率比单媒体嵌入提高了12%以上。

探究多模态嵌入在语言属性中的应用：视觉-语义案例