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

ViTA: Visual-Linguistic Translation by Aligning Object Tags