Active Deep Learning for Natural Language Processing
Bachelor Thesis, Master Thesis
Recently, deep learning gained much popularity. However, deep learning methods suffer from data sparsity – especially in NLP, where data is often cost-intensive to label. While active incremental learning approaches cope with this, they are not very well explored for deep learning models and most previous work has been done on convolutional neural networks for computer vision tasks. Furthermore, there is still only little support for such methods in the most common deep learning libraries.
The goal of this thesis is to develop an active learning extension for a popular deep learning framework, such as PyTorch or Tensorflow, and compare different strategies for a natural language learning task.