Computational study of active and interactive word learning

Abstract: Models of cross-situational word learning typically characterize the learner as a passive observer. However, a language learning child can actively participate in verbal and non-verbal communication. We present a computational model that learns to map words to objects in images through word comprehension and production. The productive and receptive parts of the model can operate independently, but can also feed into each other. This introspective quality enables the model to learn through self-supervision, and also to estimate its own word knowledge, select optimal input, and thereby alter its own learning trajectory. The modular set-up is also suitable for testing effects of communicative feedback. In this talk, I will cover our findings regarding active selection of input, and present preliminary results on tests with communicative feedback.