LifeLong@ASRU2019: Life-Long Learning for Spoken Language Systems Workshop @ ASRU 2019 Sentosa, Singapore, December 14, 2019 |
| Conference website | https://sites.google.com/view/life-long-learning-asru19/home |
| Submission link | https://easychair.org/conferences/?conf=lifelong2019 |
| Abstract registration deadline | October 28, 2019 |
| Submission deadline | October 28, 2019 |
Life Long Learning for Spoken Language Systems Workshop will bring together experts in spoken language systems whose research focuses on solving problems related to continual improvement of speech processing systems such as conversational AI. Specifically, it will provide attendees with an overview of existing approaches from various disciplines including but not limited to active learning, few-shot learning, data augmentation, and enable them to distill principles that can be more generally applicable. It will also discuss the main challenges arising in bringing speech technology systems to masses and continuous improvement of such systems. The target audience consists of researchers and practitioners in related areas.
Submission Guidelines
Format: Submissions must be in PDF format, anonymized for review, written in English and follow the ASRU 2019 formatting requirements, available here. We strongly advise you use the LaTeX template files provided by ASRU 2019.
Length: Submissions consist of up to eight pages of content. There is no limit on the number of pages for references. There is no extra space for appendices. Accepted papers will be given one additional page for content. There is no explicit short paper track, but you should feel free to submit your paper regardless of its length. Reviewers will be instructed not to penalize papers for being too short.
Dual Submission: Authors can make submissions that are also under review at other venues, provided it does not violate the policy at those venues.We do NOT require submissions to follow an anonymity period.
List of Topics
- Semi-supervised learning
- Active learning
- Unsupervised learning
- Incremental learning
- Domain adaptation
- Data generation/augmentation
- Few shot learning
- Zero shot learning
Committees
Organizing committee
- William M. Campbell, Alexa AI, Amazon
- Alex Waibel, Carnegie Mellon University/Karlsruhe Institute of Technology
- Dilek Hakkani-Tur, Alexa AI, Amazon
- Timothy J. Hazen, Microsoft
- Kevin Kilgour, Google Research
- Eunah Cho, Alexa AI, Amazon
- Varun Kumar, Alexa AI, Amazon
- Hadrien Glaude, Alexa AI, Amazon
Contact
All questions about submissions should be emailed to life-long-learning-asru19@googlegroups.com