Generative Encoder Decoder Models for Task Oriented Spoken Dialog Systems with Chatting Capability
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.
SIGDIAL Conference