  Simultaneous machine translation (SiMT) aims to translate a continuous input
text stream into another language with the lowest latency and highest quality
possible. The translation thus has to start with an incomplete source text,
which is read progressively, creating the need for anticipation. In this paper,
we seek to understand whether the addition of visual information can compensate
for the missing source context. To this end, we analyse the impact of different
multimodal approaches and visual features on state-of-the-art SiMT frameworks.
Our results show that visual context is helpful and that visually-grounded
models based on explicit object region information are much better than
commonly used global features, reaching up to 3 BLEU points improvement under
low latency scenarios. Our qualitative analysis illustrates cases where only
the multimodal systems are able to translate correctly from English into
gender-marked languages, as well as deal with differences in word order, such
as adjective-noun placement between English and French.
