Abstract
In the past decade, goal-oriented spoken dialogue systems have been the most prominent component in today's virtual personal assistants. The classic dialogue systems have rather complex and/or modular pipelines. The advance of deep learning technologies has recently risen the applications of neural models to dialogue modeling. However, how to successfully apply deep learning based approaches to a dialogue system is still challenging. Hence, this tutorial is designed to focus on an overview of the dialogue system development while describing most recent research for building dialogue systems and summarizing the challenges, in order to allow researchers to study the potential improvements of the state-of-the-art dialogue systems. The tutorial material is available at http://deepdialogue.miulab.tw.- Anthology ID:
- P17-5004
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8–14
- Language:
- URL:
- https://aclanthology.org/P17-5004
- DOI:
- Cite (ACL):
- Yun-Nung Chen, Asli Celikyilmaz, and Dilek Hakkani-Tür. 2017. Deep Learning for Dialogue Systems. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts, pages 8–14, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Deep Learning for Dialogue Systems (Chen et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/P17-5004.pdf