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

Like a bilingual baby: The advantage of visually grounding a bilingual language model