# Text2Math: End-to-end Parsing Text into Math Expressions

arXiv: https://arxiv.org/abs/1910.06571 · HTML: https://mlsanity.com/paper/1910.06571/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2019.

## Built on by (most cited first, 13 of 13)

- [Investigating the Limitations of Transformers with Simple Arithmetic Tasks](https://mlsanity.com/paper/2102.13019/) (2021)
- [MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving](https://mlsanity.com/paper/2107.13435/) (2021)
- [Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints](https://mlsanity.com/paper/2109.04546/) (2021)
- [Measuring and Improving BERT's Mathematical Abilities by Predicting the Order of Reasoning](https://mlsanity.com/paper/2106.03921/) (2021)
- [Interpretable Math Word Problem Solution Generation Via Step-by-step Planning](https://mlsanity.com/paper/2306.00784/) (2023)
- [Tree-Based Representation and Generation of Natural and Mathematical Language](https://mlsanity.com/paper/2302.07974/) (2023)
- [Induced Natural Language Rationales and Interleaved Markup Tokens Enable Extrapolation in Large Language Models](https://mlsanity.com/paper/2208.11445/) (2022)
- [Math Operation Embeddings for Open-ended Solution Analysis and Feedback](https://mlsanity.com/paper/2104.12047/) (2021)
- [Generalizing Math Word Problem Solvers via Solution Diversification](https://mlsanity.com/paper/2212.00833/) (2022)
- [Math Word Problem Solving by Generating Linguistic Variants of Problem Statements](https://mlsanity.com/paper/2306.13899/) (2023)
- [ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision](https://mlsanity.com/paper/2210.07017/) (2022)
- [Textual Enhanced Contrastive Learning for Solving Math Word Problems](https://mlsanity.com/paper/2211.16022/) (2022)
- [Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery](https://mlsanity.com/paper/2606.08728/) (2026)

## Abstract (arXiv)

We propose Text2Math, a model for semantically parsing text into math expressions. The model can be used to solve different math related problems including arithmetic word problems and equation parsing problems. Unlike previous approaches, we tackle the problem from an end-to-end structured prediction perspective where our algorithm aims to predict the complete math expression at once as a tree structure, where minimal manual efforts are involved in the process. Empirical results on benchmark datasets demonstrate the efficacy of our approach.