Advances in Few-Shot Learning on Text Data


Deep learning approaches suffer from poor sampling efficiency in contrast to human perception - even a child could recognize an exotic animal when it sees a single image. One- and few-shot learning tries to learn representations from only a few samples and is often used in image recognition, such as facial recognition, where only few data and targets are available. Recently researchers started to use these techniques also in linguistic data. This is of particular interest, as a chronic lack of data in the field of deep learning in medicine is a constantly challenge.

Task In this work, the student(s) will identify and structure the latest research in the field of n-shot learning, in particular, with a focus on topic, aspect detection, sentiment analysis and linguistic health data.
Utilises None, literature review only
Requirements Preliminary knowledge in neural networks and representation learning (word embeddings)
Languages German or English
Supervisor Lukas Stappen, M. Sc. (