@inproceedings{chaturvedi-etal-2018-cnn,
    title = "{CNN} for Text-Based Multiple Choice Question Answering",
    author = "Chaturvedi, Akshay  and
      Pandit, Onkar  and
      Garain, Utpal",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/P18-2044/",
    doi = "10.18653/v1/P18-2044",
    pages = "272--277",
    abstract = "The task of Question Answering is at the very core of machine comprehension. In this paper, we propose a Convolutional Neural Network (CNN) model for text-based multiple choice question answering where questions are based on a particular article. Given an article and a multiple choice question, our model assigns a score to each question-option tuple and chooses the final option accordingly. We test our model on Textbook Question Answering (TQA) and SciQ dataset. Our model outperforms several LSTM-based baseline models on the two datasets."
}