文法メタ知識による語彙学習加速のコネクショニストモデル

抄録

In the infancy of human being, it is known that the number of words in speech increase drastically. We think a word acquisition boosting of this period occurs according to the fast mapping in the learning system which is controlled by a meta-information about the language situation. To explain the boosting mechanism, we propose a neural network model of the meta-information that consists of a prediction part, which is a simple recurrent neural network, and a learning evaluation part that controls the fast learning. The learning evaluation part learns a confidence of learning progress as the meta-information from a representation of recurrent network. By a computer simulation study, we show that the meta-information is learnable in spite of its luck of saliency and that the use of meta-information results accelerative learning.