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In this paper, we investigate the effectiveness of training a multimodal neural machine translation (MNMT) system with image features for a lowresource language pair, Hindi and English, using synthetic data. A threeway parallel corpus which contains bilingual texts and corresponding images is required to train a MNMT system with image features. However, such a corpus is not available for low resource language pairs. To address this, we developed both a synthetic training dataset and a manuallydoi:10.18653/v1/w18-3405 dblp:conf/acl-deeplo/ChowdhuryHL18 fatcat:hmav4zatmzgazjx3wzq2nox74i