Code for Graph-to-Tree Learning for Solving Math Word Problems (ACL 2020)

Overview

Graph-to-Tree Learning for Solving Math Word Problems

PyTorch implementation of Graph based Math Word Problem solver described in our ACL 2020 paper Graph-to-Tree Learning for Solving Math Word Problems. In this work, we propose a solution for Math Word Problem Solving via graph neural network.

Steps to run the experiments

Requirements

  • Python 3.6
  • >= PyTorch 1.0.0

For more details, please refer to requiremnt file.

Training

[MATH23K]

first get into the math23k directory:

  • cd math23k

training-test setting :

  • python run_seq2tree_graph.py

cross-validation setting :

  • python cross_valid_graph2tree.py

[MAWPS]

cross-validation setting :

  • cd mawps
  • python cross_valid_mawps.py

Contact

  • As I have graduated from my master school, my school email address will be invalid soon. If you have further questions about our work, you can refer to my new email address [email protected].

Reference

@article{zhang2020graph2tree,
  title={Graph-to-Tree Learning for Solving Math Word Problems},
  author={Zhang, Jipeng and Wang, Lei and Lee, Roy Ka-Wei and Bin, Yi and Shao, Jie and Lim, Ee-Peng},
  journal={ACL 2020},
  year={2020}
}
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Comments
  • I have an error and would like to know how to remedy it.

    I have an error and would like to know how to remedy it.

    Hello. I am a university student studying artificial intelligence. I am studying with your code as a reference. I got an error in the last code "encoder_outputs, problem_output = encoder(input_var, input_length)" of the 59th execution of the published "seq2tree_analysis.ipynb" code. The error code appears as "forward() missing 1 required positional argument: 'batch_graph'". I would be happy to hear if there is any improvement.

    opened by Natu-ja 1
Owner
Jipeng Zhang
Jipeng Zhang
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