Generative Encoder-decoder Models For Task-oriented Spoken Dialog Systems With Chatting Capability
Tiancheng Zhao, Allen Lu, Kyusong Lee, Maxine Eskenazi . Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue 2017 – 87 citations
Generative encoder-decoder models offer great promise in developing domain-general dialog systems. However, they have mainly been applied to open-domain conversations. This paper presents a practical and novel framework for building task-oriented dialog systems based on encoder-decoder models. This framework enables encoder-decoder models to accomplish slot-value independent decision-making and interact with external databases. Moreover, this paper shows the flexibility of the proposed method by interleaving chatting capability with a slot-filling system for better out-of-domain recovery. The models were trained on both real-user data from a bus information system and human-human chat data. Results show that the proposed framework achieves good performance in both offline evaluation metrics and in task success rate with human users.