Abstract
In the past decade, spoken dialogue systems have been the most prominent component in today’s personal assistants. A lot of devices have incorporated dialogue system modules, which allow users to speak naturally in order to finish tasks more efficiently. The traditional conversational 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. Nevertheless, applying deep learning technologies for building robust and scalable dialogue systems is still a challenging task and an open research area as it requires deeper understanding of the classic pipelines as well as detailed knowledge on the benchmark of the models of the prior work and the recent state-of-the-art work. Therefore, this tutorial is designed to focus on an overview of the dialogue system development while describing most recent research for building task-oriented and chit-chat dialogue systems, and summarizing the challenges. We target the audience of students and practitioners who have some deep learning background, who want to get more familiar with conversational dialogue systems.- Anthology ID:
- I17-5003
- Volume:
- Proceedings of the IJCNLP 2017, Tutorial Abstracts
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 6–10
- Language:
- URL:
- https://aclanthology.org/I17-5003
- DOI:
- Cite (ACL):
- Yun-Nung Chen and Jianfeng Gao. 2017. Open-Domain Neural Dialogue Systems. In Proceedings of the IJCNLP 2017, Tutorial Abstracts, pages 6–10, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Open-Domain Neural Dialogue Systems (Chen & Gao, IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/I17-5003.pdf