Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning
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Recently there has been a lot of interest in learning common representations for multiple views of data. Typically, such common representations are learned using a parallel corpus between the two views (say, 1M images and their English captions). In this work, we address a real-world scenario where no direct parallel data is available between two views of interest (say, V_1 and V_2) but parallel data is available between each of these views and a pivot view (V_3). We propose a model for learning a common representation for V_1, V_2 and V_3 using only the parallel data available between V_1V_3 and V_2V_3. The proposed model is generic and even works when there are n views of interest and only one pivot view which acts as a bridge between them. There are two specific downstream applications that we focus on (i) transfer learning between languages L_1,L_2,...,L_n using a pivot language L and (ii) cross modal access between images and a language L_1 using a pivot language L_2. Our model achieves state-of-the-art performance in multilingual document classification on the publicly available multilingual TED corpus and promising results in multilingual multimodal retrieval on a new dataset created and released as a part of this work.
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