Lifelong and Continual Learning Dialogue Systems

Learn new knowledge during conversation: learning on the job


New Book: "Lifelong Machine Learning." by Z. Chen and B. Liu, Morgan & Claypool Publishers, 2018 (2nd edition).

Continual Learning Dialogue Systems - Learning on the Job after Model Deployment. Tutorial @ IJCAI-2021, August 21-26, 2021, Montreal, Canada.
Continual Learning Dialogue Systems - Learning after Model Deployment. Invited talk @ ICLR-21 Workshop on Neural Conversational AI, May 7, 2021.
Learning on the Job in the Open World. Invited talk @ ICML-2020 Workshop on Continual Learning, July 17, 2020.
Lifelong Machine Learning Tutorial. Title: lifelong machine learning and computer reading the Web, KDD-2016, August 13-17, 2016, San Francisco, USA.
Lifelong Machine Learning Tutorial, IJCAI-2015, July 25-31, 2015, Buenos Aires, Argentina.

Although dialogue systems (chatbots) have been very popular in recent years, they still have some serious weaknesses which limit the scope of their applications. One major weakness is that they cannot learn new knowledge during the conversation process, i.e., their knowledge is fixed beforehand and cannot be expanded or updated during conversation. In this work, we propose to build a general knowledge learning engine for chatbots to enable them to continuously and interactively learn new knowledge during conversations. As time goes by, they become more and more knowledgeable and better and better at learning and conversation. This learning process is like human learning on the job.

Publications

         TextBook: Zhiyuan Chen and Bing Liu. Lifelong Machine Learning. Morgan & Claypool, 2016.

Created on Jan 24, 2018 by Bing Liu.