# Neural Arithmetic Expression Calculator

arXiv: https://arxiv.org/abs/1809.08590 · HTML: https://mlsanity.com/paper/1809.08590/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [Logical Tasks for Measuring Extrapolation and Rule Comprehension](https://mlsanity.com/paper/2211.07727/) (2022)
- [Enhancing Geometric Perception in VLMs via Translator-Guided Reinforcement Learning](https://mlsanity.com/paper/2602.22703/) (2026)

## Abstract (arXiv)

This paper presents a pure neural solver for arithmetic expression calculation (AEC) problem. Previous work utilizes the powerful capabilities of deep neural networks and attempts to build an end-to-end model to solve this problem. However, most of these methods can only deal with the additive operations. It is still a challenging problem to solve the complex expression calculation problem, which includes the adding, subtracting, multiplying, dividing and bracketing operations. In this work, we regard the arithmetic expression calculation as a hierarchical reinforcement learning problem. An arithmetic operation is decomposed into a series of sub-tasks, and each sub-task is dealt with by a skill module. The skill module could be a basic module performing elementary operations, or interactive module performing complex operations by invoking other skill models. With curriculum learning, our model can deal with a complex arithmetic expression calculation with the deep hierarchical structure of skill models. Experiments show that our model significantly outperforms the previous models for arithmetic expression calculation.