Encoding Word Confusion Networks with Recurrent Neural Networks for Dialog State Tracking
This paper presents our novel method to encode word confusion networks, which can represent a rich hypothesis space of automatic speech recognition systems, via recurrent neural networks. We demonstrate the utility of our approach for the task of dialog state tracking in spoken dialog systems that relies on automatic speech recognition output. Encoding confusion networks outperforms encoding the best hypothesis of the automatic speech recognition in a neural system for dialog state tracking on the well-known second Dialog State Tracking Challenge dataset.
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Automatic Speech RecognitionAutomatic Speech Recognition (ASR)dialog state trackingspeech-recognitionSpeech RecognitionSimilar Papers 제목 키워드 기반
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