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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W17-5505.pdf