Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability

Tiancheng Zhao, Allen Lu, Kyusong Lee, Maxine Eskenazi


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.
Anthology ID:
W17-5505
Volume:
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue
Month:
August
Year:
2017
Address:
Saarbrücken, Germany
Editors:
Kristiina Jokinen, Manfred Stede, David DeVault, Annie Louis
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
27–36
Language:
URL:
https://aclanthology.org/W17-5505
DOI:
10.18653/v1/W17-5505
Bibkey:
Cite (ACL):
Tiancheng Zhao, Allen Lu, Kyusong Lee, and Maxine Eskenazi. 2017. Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability. In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pages 27–36, Saarbrücken, Germany. Association for Computational Linguistics.
Cite (Informal):
Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability (Zhao et al., SIGDIAL 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/W17-5505.pdf