@inproceedings{chaturvedi-etal-2018-cnn,
title = "{CNN} for Text-Based Multiple Choice Question Answering",
author = "Chaturvedi, Akshay and
Pandit, Onkar and
Garain, Utpal",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-2044",
doi = "10.18653/v1/P18-2044",
pages = "272--277",
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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%0 Conference Proceedings
%T CNN for Text-Based Multiple Choice Question Answering
%A Chaturvedi, Akshay
%A Pandit, Onkar
%A Garain, Utpal
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F chaturvedi-etal-2018-cnn
%X 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.
%R 10.18653/v1/P18-2044
%U https://aclanthology.org/P18-2044
%U https://doi.org/10.18653/v1/P18-2044
%P 272-277
Markdown (Informal)
[CNN for Text-Based Multiple Choice Question Answering](https://aclanthology.org/P18-2044) (Chaturvedi et al., ACL 2018)
ACL
- Akshay Chaturvedi, Onkar Pandit, and Utpal Garain. 2018. CNN for Text-Based Multiple Choice Question Answering. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 272–277, Melbourne, Australia. Association for Computational Linguistics.