  There are thousands of actively spoken languages on Earth, but a single
visual world. Grounding in this visual world has the potential to bridge the
gap between all these languages. Our goal is to use visual grounding to improve
unsupervised word mapping between languages. The key idea is to establish a
common visual representation between two languages by learning embeddings from
unpaired instructional videos narrated in the native language. Given this
shared embedding we demonstrate that (i) we can map words between the
languages, particularly the 'visual' words; (ii) that the shared embedding
provides a good initialization for existing unsupervised text-based word
translation techniques, forming the basis for our proposed hybrid visual-text
mapping algorithm, MUVE; and (iii) our approach achieves superior performance
by addressing the shortcomings of text-based methods -- it is more robust,
handles datasets with less commonality, and is applicable to low-resource
languages. We apply these methods to translate words from English to French,
Korean, and Japanese -- all without any parallel corpora and simply by watching
many videos of people speaking while doing things.
