Machine learning has become the dominant approach to building natural-language processing systems. However, current approaches generally require a great deal of laboriously constructed humanannotated training data. Ideally, a computer would be able to acquire language like a child by being exposed to linguistic input in the context of a relevant but ambiguous perceptual environment. As a step in this direction, we have developed systems that learn to sportscast simulated robot soccer games and to follow navigation instructions in virtual environments by simply observing sample human linguistic behavior in context. .
