Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
1-2019
Abstract
The design of automatic solvers to arithmetic math word problems has attracted considerable attention in recent years and a large number of datasets and methods have been published. Among them, Math23K is the largest data corpus that is very helpful to evaluate the generality and robustness of a proposed solution. The best performer in Math23K is a seq2seq model based on LSTM to generate the math expression. However, the model suffers from performance degradation in large space of target expressions. In this paper, we propose a template-based solution based on recursive neural network for math expression construction. More specifically, we first apply a seq2seq model to predict a tree-structure template, with inferred numbers as leaf nodes and unknown operators as inner nodes. Then, we design a recursive neural network to encode the quantity with Bi-LSTM and self attention, and infer the unknown operator nodes in a bottom-up manner. The experimental results clearly establish the superiority of our new framework as we improve the accuracy by a wide margin in two of the largest datasets, i.e., from 58.1% to 66.9% in Math23K and from 62.8% to 66.8% in MAWPS.
Discipline
Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 33rd 2019 AAAI Conference on Artificial Intelligence: Honolulu, January 27 - February 1
Volume
33
First Page
7144
Last Page
7151
ISBN
9781577358091
Identifier
10.1609/aaai.v33i01.33017144
Publisher
AAAI Press
City or Country
Palo Alto, CA
Citation
WANG, Lei; ZHANG, Dongxiang; ZHANG, Jipeng; XU, Xing; GAO, Lianli; DAI, Bing Tian; and SHEN, Heng Tao.
Template-based math word problem solvers with recursive neural networks. (2019). Proceedings of the 33rd 2019 AAAI Conference on Artificial Intelligence: Honolulu, January 27 - February 1. 33, 7144-7151.
Available at: https://ink.library.smu.edu.sg/sis_research/4866
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Additional URL
https://doi.org/10.1609/aaai.v33i01.33017144
Included in
Artificial Intelligence and Robotics Commons, Numerical Analysis and Scientific Computing Commons