Image-Mediated Learning for Zero-Shot Cross-Lingual Document Retrieval

Ruka Funaki, Hideki Nakayama

Abstract: We propose an image-mediated learning approach for cross-lingual document retrieval where no or only a few parallel corpora are available. Using the images in image-text documents of each language as the hub, we derive a common semantic subspace bridging two languages by means of generalized canonical correlation analysis. For the purpose of evaluation, we create and release a new document dataset consisting of three types of data (English text, Japanese text, and images). Our approach substantially enhances retrieval accuracy in zero-shot and few-shot scenarios where text-to-text examples are scarce.