Intentional context in situated natural language learning

Natural language interfaces designed for situationally embedded domains (e.g. cars, videogames) must incorporate knowledge about the users ’ context to address the many ambiguities of situated language use. We introduce a model of situated language acquisition that operates in two phases. First, intentional context is represented and inferred from user actions using probabilistic context free grammars. Then, utterances are mapped onto this representation in a noisy channel framework. The acquisition model is trained on unconstrained speech collecte