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.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},
}