Papers › Deep Neural Solver for Math Word Problems
Deep Neural Solver for Math Word Problems
Yan Wang, Xiaojiang Liu, Shuming Shi
This paper presents a deep neural solver to automatically solve math word problems. In contrast to previous statistical learning approaches, we directly translate math word problems to equation templates using a recurrent neural network (RNN) model, without sophisticated feature engineering. We further design a hybrid model that combines the RNN model and a similarity-based retrieval model to achieve additional performance improvement. Experiments conducted on a large dataset show that the RNN model and the hybrid model significantly outperform state-of-the-art statistical learning methods for math word problem solving.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Math Word Problem Solving | ALG514 | ZDC | Accuracy (%) | 79.7 | #4 of 10 | Archive leaderboard | report |
| Math Word Problem Solving | Math23K | Hybrid model w/ SNI | Accuracy (5-fold) | 64.7 | #15 of 19 | Archive leaderboard | report |
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