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?