Math Word Problem Solving
61 papers with code • 11 benchmarks • 17 datasets
A math word problem is a mathematical exercise (such as in a textbook, worksheet, or exam) where significant background information on the problem is presented in ordinary language rather than in mathematical notation. As most word problems involve a narrative of some sort, they are sometimes referred to as story problems and may vary in the amount of technical language used.
Benchmarks
These leaderboards are used to track progress in Math Word Problem Solving
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Math Word Problem Solving models and implementations
MATH
SVAMP
ASDiv
MathQA
MathVista
IconQA
ALG514
Latest papers with no code
Generating Equation by Utilizing Operators : GEO model
In this paper, we propose the GEO (Generation of Equations by utilizing Operators) model that does not use hand-crafted features and addresses two issues that are present in existing neural models: 1. missing domain-specific knowledge features and 2. losing encoder-level knowledge.
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
We introduce a new representation language to model precise operation programs corresponding to each math problem that aim to improve both the performance and the interpretability of the learned models.
An Improved Coarse-to-Fine Method for Solving Generation Tasks
The methods first generate a rough sketch in the coarse stage and then use the sketch to get the final result in the fine stage.
Translating a Math Word Problem to a Expression Tree
Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving.
Neural Math Word Problem Solver with Reinforcement Learning
Experimental results show that (1) The copy and alignment mechanism is effective to address the two issues; (2) Reinforcement learning leads to better performance than maximum likelihood on this task; (3) Our neural model is complementary to the feature-based model and their combination significantly outperforms the state-of-the-art results.
Using Intermediate Representations to Solve Math Word Problems
To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system.
Learning Fine-Grained Expressions to Solve Math Word Problems
This method learns the mappings between math concept phrases in math word problems and their math expressions from training data.
Deep Neural Solver for Math Word Problems
This paper presents a deep neural solver to automatically solve math word problems.
