Incremental LSTM-based Dialog State Tracker
This work addresses dialog state tracking for spoken dialog systems, but it is incremental as it builds on existing LSTM methods with minor improvements.
The authors tackled the problem of dialog state tracking in spoken dialog systems by introducing an incremental LSTM-based tracker that uses automatic speech recognition hypotheses, achieving performance close to state-of-the-art.
A dialog state tracker is an important component in modern spoken dialog systems. We present an incremental dialog state tracker, based on LSTM networks. It directly uses automatic speech recognition hypotheses to track the state. We also present the key non-standard aspects of the model that bring its performance close to the state-of-the-art and experimentally analyze their contribution: including the ASR confidence scores, abstracting scarcely represented values, including transcriptions in the training data, and model averaging.