  While most machine translation systems to date are trained on large parallel
corpora, humans learn language in a different way: by being grounded in an
environment and interacting with other humans. In this work, we propose a
communication game where two agents, native speakers of their own respective
languages, jointly learn to solve a visual referential task. We find that the
ability to understand and translate a foreign language emerges as a means to
achieve shared goals. The emergent translation is interactive and multimodal,
and crucially does not require parallel corpora, but only monolingual,
independent text and corresponding images. Our proposed translation model
achieves this by grounding the source and target languages into a shared visual
modality, and outperforms several baselines on both word-level and
sentence-level translation tasks. Furthermore, we show that agents in a
multilingual community learn to translate better and faster than in a bilingual
communication setting.
