  Recent advances in natural language processing (NLP) have led to strong text
classification models for many tasks. However, still often thousands of
examples are needed to train models with good quality. This makes it
challenging to quickly develop and deploy new models for real world problems
and business needs. Few-shot learning and active learning are two lines of
research, aimed at tackling this problem. In this work, we combine both lines
into FASL, a platform that allows training text classification models using an
iterative and fast process. We investigate which active learning methods work
best in our few-shot setup. Additionally, we develop a model to predict when to
stop annotating. This is relevant as in a few-shot setup we do not have access
to a large validation set.
