  Neural Machine Translation (NMT) is a predominant machine translation
technology nowadays because of its end-to-end trainable flexibility. However,
NMT still struggles to translate properly in low-resource settings specifically
on distant language pairs. One way to overcome this is to use the information
from other modalities if available. The idea is that despite differences in
languages, both the source and target language speakers see the same thing and
the visual representation of both the source and target is the same, which can
positively assist the system. Multimodal information can help the NMT system to
improve the translation by removing ambiguity on some phrases or words. We
participate in the 8th Workshop on Asian Translation (WAT - 2021) for
English-Hindi multimodal translation task and achieve 42.47 and 37.50 BLEU
points for Evaluation and Challenge subset, respectively.
