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arXiv · 1910.03291

Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task

Alireza Mohammadshahi·Remi Lebret·Karl Aberer
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Abstract

In this paper, we propose a new approach to learn multimodal multilingual embeddings for matching images and their relevant captions in two languages. We combine two existing objective functions to make images and captions close in a joint embedding space while adapting the alignment of word embeddings between existing languages in our model. We show that our approach enables better generalization, achieving state-of-the-art performance in text-to-image and image-to-text retrieval task, and caption-caption similarity task. Two multimodal multilingual datasets are used for evaluation: Multi30k with German and English captions and Microsoft-COCO with English and Japanese captions.

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Alireza Mohammadshahi, Remi Lebret, Karl Aberer. 2019-10-08. Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task. https://doi.org/10.18653/v1%2Fd19-6605

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