@InProceedings{chaturvedi-pandit-garain:2018:Short,
  author    = {Chaturvedi, Akshay  and  Pandit, Onkar  and  Garain, Utpal},
  title     = {CNN for Text-Based Multiple Choice Question Answering},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/P18-2044}
}