Active Word Learning through Self-supervision

Models of cross-situational word learning typically characterize the learner as a passive observer, but a language learning child can actively participate in verbal and non-verbal communication. We present a computational study of crosssituational word learning to investigate whether a curious word
learner who actively influences linguistic input in each context has an advantage over a passive learner. Our computational model learns to map words to objects in real images by selfsupervision through simulating both word comprehension and production. We examine different curiosity measures as guiding input selection, and analyze the relative impact of each method. Our results suggest that active learning leads to higher overall performance, and a formulation of curiosity which relies both on subjective novelty and plasticity yields the best performance and learning stability.