CNN for text-based multiple choice question answering

Authors

Akshay Chaturvedi, Indian Statistical Institute, Kolkata
Onkar Pandit, INRIA Institut National de Recherche en Informatique et en Automatique
Utpal Garain, Indian Statistical Institute, Kolkata

Document Type

Conference Article

Publication Title

Proceedings of the Annual Meeting of the Association for Computational Linguistics, ACL 2018

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.

First Page

272

Last Page

277

DOI

10.18653/v1/p18-2044

Publication Date

1-1-2018

Comments

Open Access, Hybrid Gold, Green

Recommended Citation

Chaturvedi, Akshay; Pandit, Onkar; and Garain, Utpal, "CNN for text-based multiple choice question answering" (2018). Conference Articles. 118.
https://digitalcommons.isical.ac.in/conf-articles/118