A Goal-Driven Tree-Structured Neural Model for Math Word Problems

This repository is the PyTorch implementation for the IJCAI 2019 accepted paper:

Zhipeng Xie* and Shichao Sun*, A Goal-Driven Tree-Structured Neural Model for Math Word Problems IJCAI 2019.

* indicates equal contribution.

Seq2Tree Model

A Seq2Tree Neural Network containing top-down Recursive Neural Network and bottom-up Recursive Neural Network

Requirements

• python 3
• PyTorch 0.4.1

Train and Test

• Math23K:

python3 run_seq2tree_math23k.py

• Roth1K:

python3 run_seq2tree_roth.py

Results

Model	Accuracy
Hybrid model w/ SNI	64.7%
Ensemble model w/ EN	68.4%
Seq2Tree w/o Bottom-up RvNN	70.0%
Seq2Tree	74.3%

Citation

@inproceedings{ijcai2019-736,
  title     = {A Goal-Driven Tree-Structured Neural Model for Math Word Problems},
  author    = {Xie, Zhipeng and Sun, Shichao},
  booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on
               Artificial Intelligence, {IJCAI-19}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},             
  pages     = {5299--5305},
  year      = {2019},
  month     = {7},
  doi       = {10.24963/ijcai.2019/736},
  url       = {https://doi.org/10.24963/ijcai.2019/736},
}