AI Seminar: Natural Language Understanding with Limited Training Data
Speaker
Isabelle Augenstein, Assistant Professor in the Machine Learning Section at Department of Computer Science, University of Copenhagen.
Abstract
Natural Language Understanding refers to NLP tasks that require a deep understanding of language, as opposed to shallow, surface-form processing. Most existing research in this area focuses on studying deep learning methods that require large amounts of training data. When labelled training data is not readily available, approaches to utilise knowledge from other sources, such as related tasks or training instances, are key.
In this talk, I will present my recent and ongoing work in the space of learning with limited labelled data in NLU, including research on stance detection, fact checking, information extraction; as well as zero-shot, multi-task and transfer learning.
This seminar is a part of the AI Seminar Series organised by SCIENCE AI Centre. The series highlights advances and challenges in research within Machine Learning, Data Science, and AI. Like the AI Centre itself, the seminar series has a broad scope, covering both new methodological contributions, ground-breaking applications, and impacts on society.