@InProceedings{hedderich-klakow:2018:W18-34,
  author    = {Hedderich, Michael A.  and  Klakow, Dietrich},
  title     = {Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data},
  booktitle = {Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP},
  month     = {July},
  year      = {2018},
  address   = {Melbourne},
  publisher = {Association for Computational Linguistics},
  pages     = {12--18},
  abstract  = {Manually labeled corpora are expensive to create and often not available for low-resource languages or domains. Automatic labeling approaches are an alternative way to obtain labeled data in a quicker and cheaper way. However, these labels often contain more errors which can deteriorate a classifier's performance when trained on this data. We propose a noise layer that is added to a neural network architecture. This allows modeling the noise and train on a combination of clean and noisy data. We show that in a low-resource NER task we can improve performance by up to 35% by using additional, noisy data and handling the noise.},
  url       = {http://www.aclweb.org/anthology/W18-3402}
}