@inproceedings{juhasz-etal-2019-tuefact,
    title = "{T}ue{F}act at {S}em{E}val 2019 Task 8: Fact checking in community question answering forums: context matters",
    author = "Juh{\'a}sz, R{\'e}ka  and
      Linnenschmidt, Franziska Barbara  and
      Roys, Teslin",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/S19-2206/",
    doi = "10.18653/v1/S19-2206",
    pages = "1176--1179",
    abstract = "The SemEval 2019 Task 8 on Fact-Checking in community question answering forums aimed to classify questions into categories and verify the correctness of answers given on the QatarLiving public forum. The task was divided into two subtasks: the first classifying the question, the second the answers. The TueFact system described in this paper used different approaches for the two subtasks. Subtask A makes use of word vectors based on a bag-of-word-ngram model using up to trigrams. Predictions are done using multi-class logistic regression. The official SemEval result lists an accuracy of 0.60. Subtask B uses vectorized character n-grams up to trigrams instead. Predictions are done using a LSTM model and achieved an accuracy of 0.53 on the final SemEval Task 8 evaluation set."
}