# Can Neural Networks Learn Symbolic Rewriting?

arXiv: https://arxiv.org/abs/1911.04873 · HTML: https://mlsanity.com/paper/1911.04873/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2019.

## Built on by (most cited first, 12 of 12)

- [Symbolic Brittleness in Sequence Models: on Systematic Generalization in Symbolic Mathematics](https://mlsanity.com/paper/2109.13986/) (2021)
- [Simplifying Polylogarithms with Machine Learning](https://mlsanity.com/paper/2206.04115/) (2022)
- [Analyzing the Nuances of Transformers' Polynomial Simplification Abilities](https://mlsanity.com/paper/2104.14095/) (2021)
- [Alien Coding](https://mlsanity.com/paper/2301.11479/) (2023)
- [MizAR 60 for Mizar 50](https://mlsanity.com/paper/2303.06686/) (2023)
- [Learning Equational Theorem Proving](https://mlsanity.com/paper/2102.05547/) (2021)
- [Generating Elementary Integrable Expressions](https://mlsanity.com/paper/2306.15572/) (2023)
- [Symbolic Integration Algorithm Selection with Machine Learning: LSTMs vs Tree LSTMs](https://mlsanity.com/paper/2404.14973/) (2024)
- [Learning Conjecturing from Scratch](https://mlsanity.com/paper/2503.01389/) (2025)
- [The Liouville Generator for Producing Integrable Expressions](https://mlsanity.com/paper/2406.11631/) (2024)
- [Transformers to Predict the Applicability of Symbolic Integration Routines](https://mlsanity.com/paper/2410.23948/) (2024)
- [Tree-Based Deep Learning for Ranking Symbolic Integration Algorithms](https://mlsanity.com/paper/2508.06383/) (2025)

## Abstract (arXiv)

This work investigates if the current neural architectures are adequate for learning symbolic rewriting. Two kinds of data sets are proposed for this research -- one based on automated proofs and the other being a synthetic set of polynomial terms. The experiments with use of the current neural machine translation models are performed and its results are discussed. Ideas for extending this line of research are proposed, and its relevance is motivated.