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.
@article{arxiv.1706.08476,
title = {Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability},
author = {Tiancheng Zhao and Allen Lu and Kyusong Lee and Maxine Eskenazi},
journal= {arXiv preprint arXiv:1706.08476},
year = {2017}
}