@inproceedings{hedderich-klakow-2018-training,
    title = "Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data",
    author = "Hedderich, Michael A.  and
      Klakow, Dietrich",
    editor = "Haffari, Reza  and
      Cherry, Colin  and
      Foster, George  and
      Khadivi, Shahram  and
      Salehi, Bahar",
    booktitle = "Proceedings of the Workshop on Deep Learning Approaches for Low-Resource {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-3402",
    doi = "10.18653/v1/W18-3402",
    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.",
}