Combination of finite state automata and neural network for spoken language understanding
Chai Wutiwiwatchai, Sadaoki Furui · 2003
This paper proposes a novel approach for spoken language understanding based on a combination of weighted finite state automata and an artificial neural network.The former machine acts as a robust parser, which extracts some semantic information called subframes from an input sentence, then the latter machine interprets a concept of the sentence by considering the existence of subframes and their scores obtained from the automata.With a large number of concepts handled in our mixed-initiative dialogue system, the proposed system achieves a considerable concept interpretation result on either a typed-in test set or a spoken test set.A high subframe recall rate also verifies an applicability of the proposed system.