EEH_G2T

An Edge-Enhanced Hierarchical Graph-to-Tree Network for Math Word Problem Solving

Requirement

• Python 3.6
• Pytorch 1.8.0
• numpy
• nltk
• stanfordcorenlp
• matplotlib

Train the model.

python run_seq2tree.py

Evaluate the model.

python evaluate.py

#Structure

├── README.md // help

├── data // datasets

│ ├── mawps // MAWPS dataset

│ │ └── MAWPS.json // MAWPS dataset

│ └── Math_23K.json // Math23K dataset

├── hownet // external knowledge base HowNet

│ └── cilin.txt // external knowledge base cilin

├── models // Saved Models

├── output // Test data output

│ ├── pre_data.py // data process

├── masked_cross_entropy.py // cross_entropy function

├── expressions_transfer.py // expression process

├── models.py // EEH_G2T's main model structure

├── run_seq2tree.py // train the model (Math23K default)

├── parameter.py // parameters setting (change dataset="mawps" for MAWPS training)

├── evaluate.py // evaluate the model

└── dependency_generate.py // stanford dependency tree