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.
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