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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/I17-3017.pdf