Code for NIPS2021 Paper on MATHAI4ED Workshop: "REAL2: An End-to-end Memory-augmented Solver for Math Word Problems".

REAL2: improve the effectiveness of REAL model to solve math work problems(MWP) by optimizing the memory module.

python3.6, pytorch1.2
You can install related packages directly through "pip install requirements.txt"

python3 memory_module.py

 python3 run.py --is_train --num_train_epochs 50 \
    --start_lr_decay_epoch 25 --dataset math23k \
    --retrieve_model_name cnn --retrieve_topn 10 --topk 3 

python3 run.py --dataset math23k  \
    --retrieve_model_name cnn --retrieve_topn 10 --topk 3 

To investigate the effectiveness of the trainable memory module, we implemented our framework follows the settings of REAL and only modified the framework of stage 1 part. In particular, we compare different backbone of memory module that involves TextCNN, TextRCNN, Transformer, and BERT model.

Our code is based on unilm . We thank the authors for their wonderful open-source efforts. We use the same license as unilm.