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arXiv · 1707.07601

Image Pivoting for Learning Multilingual Multimodal Representations

Spandana Gella·Rico Sennrich·Frank Keller·Mirella Lapata
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Abstract

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.

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Spandana Gella, Rico Sennrich, Frank Keller, Mirella Lapata. 2017-07-24. Image Pivoting for Learning Multilingual Multimodal Representations. https://arxiv.org/abs/1707.07601

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