| Paper | Code | Accuracy (%) | ModelName | ReleaseDate |
|---|---|---|---|---|
| Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems | 83 | MixedSP | 2016-11-01 | |
| Generating Equation by Utilizing Operators : GEO model | 82.1 | GEO | 2020-12-01 | |
| Point to the Expression: Solving Algebraic Word Problems using the Expression-Pointer Transformer Model | ✓ Link | 81.31 | EPT | |
| Deep Neural Solver for Math Word Problems | 79.7 | ZDC | 2017-09-01 | |
| Learn to Solve Algebra Word Problems Using Quadratic Programming | 79.7 | liblinear-QP | 2015-09-01 | |
| EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers | ✓ Link | 73.91 | EPT | |
| How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation | 71.2 | SIM | 2016-08-01 | |
| Learning to Automatically Solve Algebra Word Problems | 68.7 | ALLEQ | 2014-06-01 | |
| EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers | ✓ Link | 67.07 | EPT-X | |
| Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems | ✓ Link | 57.39 | SAU-Solver | 2020-10-14 |