@inproceedings{avvaru-pandey-2019-codeforthechange,
    title = "{C}ode{F}or{T}he{C}hange at {S}em{E}val-2019 Task 8: Skip-Thoughts for Fact Checking in Community Question Answering",
    author = "Avvaru, Adithya  and
      Pandey, Anupam",
    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/iwcs-25-ingestion/S19-2199/",
    doi = "10.18653/v1/S19-2199",
    pages = "1138--1143",
    abstract = "The strengths of the scalable gradient tree boosting algorithm, XGBoost and distributed sentence encoder, Skip-Thought Vectors are not explored yet by the cQA research community. We tried to apply and combine these two effective methods for finding factual nature of the questions and answers. The work also include experimentation with other popular classifier models like AdaBoost Classifier, DecisionTree Classifier, RandomForest Classifier, ExtraTrees Classifier, XGBoost Classifier and Multi-layer Neural Network. In this paper, we present the features used, approaches followed for feature engineering, models experimented with and finally the results."
}