The Dialog System and Machine Learning Group conducts fundamental research in natural language processing and related areas of machine learning, with a view towards the development of the next generation of intelligent
conversational agents. This research is currently centred around the following key problems:
- 1. Knowledge Extraction
Traditionally, research into dialogue systems has assumed that the knowledge with which a dialogue system operates is provided a priori. How can we build systems that harvest their knowledge from non-structured natural language data?
- 2. Dynamic dialogue policies
How can we build ever-learning dialogue systems that can converse about dynamically acquired knowledge?
- 3. User modelling
How can we increase the accuracy and coverage of user models in this user-centric technology?
- 4. Reward modelling
Can we include more nuanced measures such as intrinsic motivation, curiosity and sentiment to make dialogue systems more human-like?
Group Dialog Systems and Machine Learning
Building 25.12 Level 01