Dialog Systems and Machine Learning
The Dialogue Systems 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?
Topology of Word Embeddings: Singularities Reflect Polysemy
Our paper “Topology of Word Embeddings: Singularities Reflect Polysemy” has been selected to receive the Best Paper Award at *SEM 2020.
Post-doctoral Position in Dialog Modelling
We are looking for an enthusiastic and talented post-doctoral researcher to join our award-winning international research team at Heinrich Heine University Düsseldorf.
Plenary lecture "10 things you should know about dialogue”
Milica Gasic gave a plenary lecture on "10 things you should know about dialogue” at Frederick Jelinek Memorial Summer workshop on Speech and Language Technology (JSALT 2020).
Please click here to view the slides.
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