Name
Title | ||
|---|---|---|
Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.18653/v1/W17-5505 | SIGDIAL Conference |
DocType | Volume | Citations |
Conference | abs/1706.08476 | 12 |
PageRank | References | Authors |
0.71 | 26 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| tiancheng zhao | 1 | 136 | 10.62 |
| A LU | 2 | 41 | 5.47 |
| Kyusong Lee | 3 | 89 | 15.62 |
| Maxine Eskenazi | 4 | 979 | 127.53 |