Title :

CNN for Text-Based Multiple Choice Question Answering

Author(s) :

Chaturvedi, Akshay [Auteur]
Indian Statistical Institute [Kolkata]
Pandit, Onkar [Auteur]
Machine Learning in Information Networks [MAGNET]
Garain, Utpal [Auteur]
Indian Statistical Institute [Kolkata]

Conference title :

ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics

City :

Melbourne

Country :

Australie

Start date of the conference :

2018-07-15

Journal title :

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Informatique et langage [cs.CL]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL