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The study investigates the effectiveness of utilizing multimodal informationin Neural Machine Translation (NMT). While prior research focused on usingmultimodal data in low-resource scenarios, this study examines how imagefeatures impact translation when added to a large-scale, pre-trained unimodalNMT system. Surprisingly, the study finds that images might be redundant inthis context. Additionally, the research introduces synthetic noise to assesswhether images help the model deal with textual noise. Multimodal modelsslightly outperform text-only models in noisy settings, even with randomimages. The study's experiments translate from English to Hindi, Bengali, andMalayalam, outperforming state-of-the-art benchmarks significantly.Interestingly, the effect of visual context varies with source text noise: novisual context works best for non-noisy translations, cropped image featuresare optimal for low noise, and full image features work better in high-noisescenarios. This sheds light on the role of visual context, especially in noisysettings, opening up a new research direction for Noisy Neural MachineTranslation in multimodal setups. The research emphasizes the importance ofcombining visual and textual information for improved translation in variousenvironments.

Impact of Visual Context on Noisy Multimodal NMT: An Empirical Study for English to Indian Languages