CNN for Text-Based Multiple Choice Question Answering

Akshay Chaturvedi (1) , Onkar Pandit (2) , Utpal Garain (1)

1 Indian Statistical Institute [Kolkata]
2 MAGNET - Machine Learning in Information Networks

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.

Domaines: Intelligence artificielle [cs.AI]; Informatique et langage [cs.CL]

Akshay Chaturvedi, Onkar Pandit, Utpal Garain. CNN for Text-Based Multiple Choice Question Answering. ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Jul 2018, Melbourne, Australia. pp.272 - 277. ⟨hal-02265065⟩