Meaning Acquisition from Interaction Data with Fisher's Exact Test

Abstract

Proposed is an algorithm that learns the meaning of action instructions and evaluation instructions through interaction with a navigator. The navigator speaks freely, which requires learning from various kinds of few data with biased frequency and error. Utilizing Fisher's exact test, the algorithm finds the pairs of a phrase and a situation which frequently cooccurs. An experiment shows that the learning accuracy improves compared to an earlier study.