  Unlike most neural language models, humans learn language in a rich,
multi-sensory and, often, multi-lingual environment. Current language models
typically fail to fully capture the complexities of multilingual language use.
We train an LSTM language model on images and captions in English and Spanish
from MS-COCO-ES. We find that the visual grounding improves the model's
understanding of semantic similarity both within and across languages and
improves perplexity. However, we find no significant advantage of visual
grounding for abstract words. Our results provide additional evidence of the
advantages of visually grounded language models and point to the need for more
naturalistic language data from multilingual speakers and multilingual datasets
with perceptual grounding.
