End-to-end latent-variable task-oriented dialogue system with exact log-likelihood optimization

• Main Authors: Xu, H., Peng, Haiyun, Xie, H., Cambria, Erik, Zhou, L., Zheng, W.
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering; Dialogue Model; Hierarchical Encoder-Decoder
• Online Access: https://hdl.handle.net/10356/154469
• Institution: Nanyang Technological University

We propose an end-to-end dialogue model based on a hierarchical encoder-decoder, which employed a discrete latent variable to learn underlying dialogue intentions. The system is able to model the structure of utterances dominated by statistics of the language and the dependencies among utterances in dialogues without manual dialogue state design. We argue that the latent discrete variable interprets the intentions that guide machine responses generation. We also propose a model which can be refined autonomously with reinforcement learning, due to that intention selection at each dialogue turn can be formulated as a sequential decision-making process. Our experiments show that exact MLE optimized model is much more robust than neural variational inference on dialogue success rate with limited BLEU sacrifice.