CopeNLU is a Natural Language Processing research group led by Isabelle Augenstein with a focus on researching methods for tasks that require a deep understanding of language, as opposed to shallow processing. We are affiliated with the Machine Learning Section at the Department of Computer Science, University of Copenhagen, as well as NLP at the University of Copenhagen. We are interested in core methodology research on, among others, learning with limited training data; as well as applications thereof to tasks such as fact checking, knowledge base population and question answering.
Learning with limited labelled data, including multi-task learning, weakly supervised and zero-shot learning
Training models to work well for multiple languages, including low-resource ones
Determine the attitude expressed in a text towards a topic, and use this for automatic evidence-based fact checking
Automatically detecting gendered language, and to what degree attitudes towards entities are influenced by gender bias