@InProceedings{hidey-diab:2018:FEVER,
  author    = {Hidey, Christopher  and  Diab, Mona},
  title     = {Team SWEEPer: Joint Sentence Extraction and Fact Checking with Pointer Networks},
  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     = {150--155},
  abstract  = {Our model for fact checking and verification consists of two stages: 1) identifying relevant documents using lexical and syntactic features from the claim and first two sentences in the Wikipedia article and 2) jointly modeling sentence extraction and verification. As the tasks of fact checking and finding evidence are dependent on each other, an ideal model would consider the veracity of the claim when finding evidence and also find only the evidence that supports/refutes the position of the claim. We thus jointly model the second stage by using a pointer network with the claim and evidence sentence represented using the ESIM module. For stage 2, we first train both components using multi-task learning over a larger memory of extracted sentences, then tune parameters to first extract sentences and predict the relation from only the extracted sentences.},
  url       = {http://www.aclweb.org/anthology/W18-5525}
}