System and method of exploiting human-human data for spoken language understanding systems

摘要

A method is disclosed for generating labeled utterances from human-human utterances for use in training a semantic classification model for a spoken dialog system. The method comprises augmenting received human-human utterances with data that relates to call-type gaps in the human-human utterances, augmenting the received human-human utterances by placing at least one word in the human-human utterances that improves the training ability of the utterances according to the conversation patterns of the spoken dialog system, clausifying the human-human utterances, labeling the clausified and augmented human-human utterances and building the semantic classification model for the spoken dialog system using the labeled utterances.