Deep Neural Network based system for solving Arithmetic Word problems

Purvanshi Mehta, Pruthwik Mishra, Vinayak Athavale, Manish Shrivastava, Dipti Sharma


Abstract
This paper presents DILTON a system which solves simple arithmetic word problems. DILTON uses a Deep Neural based model to solve math word problems. DILTON divides the question into two parts - worldstate and query. The worldstate and the query are processed separately in two different networks and finally, the networks are merged to predict the final operation. We report the first deep learning approach for the prediction of operation between two numbers. DILTON learns to predict operations with 88.81% accuracy in a corpus of primary school questions.
Anthology ID:
I17-3017
Volume:
Proceedings of the IJCNLP 2017, System Demonstrations
Month:
November
Year:
2017
Address:
Tapei, Taiwan
Editors:
Seong-Bae Park, Thepchai Supnithi
Venue:
IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
65–68
Language:
URL:
https://aclanthology.org/I17-3017
DOI:
Bibkey:
Cite (ACL):
Purvanshi Mehta, Pruthwik Mishra, Vinayak Athavale, Manish Shrivastava, and Dipti Sharma. 2017. Deep Neural Network based system for solving Arithmetic Word problems. In Proceedings of the IJCNLP 2017, System Demonstrations, pages 65–68, Tapei, Taiwan. Association for Computational Linguistics.
Cite (Informal):
Deep Neural Network based system for solving Arithmetic Word problems (Mehta et al., IJCNLP 2017)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/I17-3017.pdf