@InProceedings{cramerus-scheffler:2019:S19-2,
  author    = {Cramerus, Rebekah  and  Scheffler, Tatjana},
  title     = {Team Kit Kittredge at SemEval-2019 Task 4: LSTM Voting System},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {1021--1025},
  abstract  = {This paper describes the approach of team Kit Kittredge to SemEval-2019 Task 4: Hyperpartisan News Detection. The goal was binary classification of news articles into the categories of "biased" or "unbiased". We had two software submissions: one a simple bag-of-words model, and the second an LSTM (Long Short Term Memory) neural network, which was trained on a subset of the original dataset selected by a voting system of other LSTMs. This method did not prove much more successful than the baseline, however, due to the models' tendency to learn publisher-specific traits instead of general bias.},
  url       = {http://www.aclweb.org/anthology/S19-2178}
}