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CNN for Text-Based Multiple Choice Question Answering

Akshay Chaturvedi 1 Onkar Pandit 2 Utpal Garain 3
2 MAGNET - Machine Learning in Information Networks
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
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.
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Submitted on : Thursday, August 8, 2019 - 12:11:21 PM
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  • HAL Id : hal-02265065, version 1


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⟩



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