Alan Akbik

Institute for Informatics – HU Berlin

Few-Shot Learning for Natural Language Processing with TARS and Flair

Machine learning models for natural language processing (NLP) are typically trained with very large amounts of labeled training data. However, such data is often not readily available and very expensive to produce. In this talk I present TARS, a novel approach in the research area of „few-shot learning“ which allows us to train text classification models with little training data – or even none at all! I show how the proposed approach can be applied to a continual learning setup in which a single model learns a number of different tasks in sequence, with the goal of learning all tasks. Finally, I give a brief overview of the Flair NLP framework (https://github.com/flairNLP/flair) we develop in my group together with the open source community, and show how you can use TARS (and other NLP components in Flair) in your own research or industry projects.