Efficient Speech Dialogue State Tracking Based on Quantum Machine Learning

Abstract

Cascade implementations of multiple machine learning models allow individual modules to be trained independently. However, they face the challenge that some information other than the final hypothesis output by the preceding module becomes partially lost at that stage. To address this issue, N-best training methods are generally employed, but they suffer from increased training and inference costs depending on N. This research proposes a framework for speech dialogue state tracking that addresses this issue. By inputting a vector composed of the probability values for each hypothesis output by the ASR model into a quantum machine learning model and simultaneously processing multiple hypotheses, it suppresses the increase in training and inference costs associated with conventional N-best training methods. We applied the proposed method to the DSTC2 dataset for speech dialogue tracking tasks and confirmed that it enables a significant reduction in the number of parameters while maintaining accuracy.