  Current work on multimodal machine translation (MMT) has suggested that the
visual modality is either unnecessary or only marginally beneficial. We posit
that this is a consequence of the very simple, short and repetitive sentences
used in the only available dataset for the task (Multi30K), rendering the
source text sufficient as context. In the general case, however, we believe
that it is possible to combine visual and textual information in order to
ground translations. In this paper we probe the contribution of the visual
modality to state-of-the-art MMT models by conducting a systematic analysis
where we partially deprive the models from source-side textual context. Our
results show that under limited textual context, models are capable of
leveraging the visual input to generate better translations. This contradicts
the current belief that MMT models disregard the visual modality because of
either the quality of the image features or the way they are integrated into
the model.
