Dialog Systems and Machine Learning
The Dialogue 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?
New publication: “TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking”
The Machine Learning and Dialog Systems Group is delighted to announce the novel publication of the paper “TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking” by Michael Heck, Carel van Niekerk, Nurul Lubis, Christian Geishauser, Hsien-Chin Lin, Marco Moresi and Milica Gašić to be published in Proceedings of the 21st Annual SIGdial Meeting on Discourse and Dialogue.
Seminar „Selected topics in Machine Learning and Natural Language Processing”
The Dialogue Systems and Machine Leaning group is pleased to announce seminar "Selected topics in Machine Learning and Natural Language Processing" to interested students.
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