CNN for text-based multiple choice question answering
Proceedings of the Annual Meeting of the Association for Computational Linguistics, ACL 2018
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.
Chaturvedi, Akshay; Pandit, Onkar; and Garain, Utpal, "CNN for text-based multiple choice question answering" (2018). Conference Articles. 118.
Open Access, Hybrid Gold, Green