@InProceedings{yoneda-EtAl:2018:FEVER,
  author    = {Yoneda, Takuma  and  Mitchell, Jeff  and  Welbl, Johannes  and  Stenetorp, Pontus  and  Riedel, Sebastian},
  title     = {UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)},
  booktitle = {Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)},
  month     = {November},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {97--102},
  abstract  = {Our system is a four stage model consisting of document retrieval, sentence retrieval, natural language inference and aggregation. Document retrieval attempts to find the name of a Wikipedia article in the claim, and then ranks each article based on capitalisation, sentence position and token match features. A set of sentences are then retrieved from the top ranked articles, based on token matches with the claim and position in the article. A natural language inference model is then applied to each of these sentences paired with the claim, giving a prediction for each potential evidence. These predictions are then aggregated using a simple MLP, and the sentences are reranked to keep only the evidence consistent with the final prediction.},
  url       = {http://www.aclweb.org/anthology/W18-5515}
}