In this paper we propose a model to learn multimodal multilingual representations for matching images and sentences in different languages, with the aim of advancing multilingual versions of image search and image understanding. Our model learns a common representation for images and their descriptions in two different languages (which need not be parallel) by considering the image as a pivot between two languages. We introduce a new pairwise ranking loss function which can handle both symmetric and asymmetric similarity between the two modalities. We evaluate our models on image-description ranking for German and English, and on semantic textual similarity of image descriptions in English. In both cases we achieve state-of-the-art performance.
@article{arxiv.1707.07601,
title = {Image Pivoting for Learning Multilingual Multimodal Representations},
author = {Spandana Gella and Rico Sennrich and Frank Keller and Mirella Lapata},
journal= {arXiv preprint arXiv:1707.07601},
year = {2017}
}