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Conference Papers Year : 2018

CNN for Text-Based Multiple Choice Question Answering

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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Dates and versions

hal-02265065 , version 1 (08-08-2019)

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  • HAL Id : hal-02265065 , version 1

Cite

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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