Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment
Yinpei Dai, Hangyu Li, Chengguang Tang, Yongbin Li, Jian Sun, Xiaodan Zhu
Abstract
Existing end-to-end dialog systems perform less effectively when data is scarce. To obtain an acceptable success in real-life online services with only a handful of training examples, both fast adaptability and reliable performance are highly desirable for dialog systems. In this paper, we propose the Meta-Dialog System (MDS), which combines the advantages of both meta-learning approaches and human-machine collaboration. We evaluate our methods on a new extended-bAbI dataset and a transformed MultiWOZ dataset for low-resource goal-oriented dialog learning. Experimental results show that MDS significantly outperforms non-meta-learning baselines and can achieve more than 90% per-turn accuracies with only 10 dialogs on the extended-bAbI dataset.- Anthology ID:
- 2020.acl-main.57
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 609–618
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.57
- DOI:
- 10.18653/v1/2020.acl-main.57
- Cite (ACL):
- Yinpei Dai, Hangyu Li, Chengguang Tang, Yongbin Li, Jian Sun, and Xiaodan Zhu. 2020. Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 609–618, Online. Association for Computational Linguistics.
- Cite (Informal):
- Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment (Dai et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.acl-main.57.pdf