Teacher-Student Networks with Multiple Decoders for Solving Math Word Problem.

PyTorch implementation of Graph based Math Word Problem solver described in our IJCAI 2020 paper Teacher-Student Networks with Multiple Decoders for Solving Math Word Problem. In this work, we propose an enhancement method for Math Word Problem Solving systems.

Steps to run the experiments

Requirements

• Python 3.6
• >= PyTorch 1.0.0

For more details, please refer to requiremnt file.

Training

We have provided the pre-processed soft target file in corresponding data directory. If you want to extract soft target from your own model, you can refer to math23k/seq2tree_save_softtarget.py and adapt it based on your code.

[MATH23K]

first get into the math23k directory:

• cd math23k

training-test setting :

• python run_seq2tree_diverse.py

cross-validation setting : It's easy to modify run_seq2tree_diverse.py and adapt it to cross-validation setting.

[MAWPS]

cross-validation setting :

• cd mawps
• python run_seq2tree_diverse.py

Reference

@article{zhang2020tsnmd,
  title={Graph-to-Tree Learning for Solving Math Word Problems},
  author={Jipeng Zhang, Roy Ka-Wei Lee, Ee-Peng Lim, Wei Qin, Lei Wang, Jie Shao and Qianru Sun},
  journal={IJCAI 2020},
  year={2020}
}