  Multimodal Machine Translation (MMT) enriches the source text with visual
information for translation. It has gained popularity in recent years, and
several pipelines have been proposed in the same direction. Yet, the task lacks
quality datasets to illustrate the contribution of visual modality in the
translation systems. In this paper, we propose our system under the team name
Volta for the Multimodal Translation Task of WAT 2021 from English to Hindi. We
also participate in the textual-only subtask of the same language pair for
which we use mBART, a pretrained multilingual sequence-to-sequence model. For
multimodal translation, we propose to enhance the textual input by bringing the
visual information to a textual domain by extracting object tags from the
image. We also explore the robustness of our system by systematically degrading
the source text. Finally, we achieve a BLEU score of 44.6 and 51.6 on the test
set and challenge set of the multimodal task.
