26 research outputs found
Incremental LSTM-based Dialog State Tracker
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
Dialogue state tracking accuracy improvement by distinguishing slot-value pairs and dialogue behaviour
Dialog state tracking (DST) plays a critical role in cycle life of a task-oriented dialogue system. DST represents the goals of the consumer at each step by dialogue and describes such objectives as a conceptual structure comprising slot-value pairs and dialogue actions that specifically improve the performance and effectiveness of dialogue systems. DST faces several challenge