Deep Learning with Humans-In-The-Loop: Active Learning for NLP

With Lukas Rauch
Kassel, July 4th 2024

The abundance of text data and the advent of powerful deep learning models has led to rapid advancements in Natural Language Processing (NLP). However, adapting models to specialized tasks still requires a nuanced understanding of the data.

This adaption often requires human-annotated data that are considered to be time-consuming and labor-intensive. Active Learning addresses this by strategically involving humans in the learning loop, selecting instances for annotation that are most likely to maximize performance gains.

This approach not only optimizes human effort but also enhances the model’s adaptability to specific tasks with fewer annotated instances, making the training process more efficient and effective.

In this full day workshop in Kassel, you will get introduced to the concepts of Human-In-The-Loop Learning and (Deep) Active Learning. In a hands-on practical session, you will design a (Deep) Active Learning Cycle using Python.



  • Basic Python Skills
  • Basic experience in Machine Learning/Deep Learning
  • Bringing your own laptop to the workshop

About the Instructor

Lukas Rauch is a researcher in the AI for Computationally Intelligent Systems (AI4CIS) team at the University of Kassel, Germany. After completing his master’s degree, he began his PhD in Natural Language Processing, with a focus on Transformer models and Deep Active Learning. Currently, he is working in the field of Avian Bioacoustics to apply these research methods to practical and challenging problems.