# Disambiguating Symbolic Expressions in Informal Documents

arXiv: https://arxiv.org/abs/2101.11716 · HTML: https://mlsanity.com/paper/2101.11716/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 62 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Analysing Mathematical Reasoning Abilities of Neural Models](https://mlsanity.com/paper/1904.01557/) (2019)
- [Deep Learning for Symbolic Mathematics](https://mlsanity.com/paper/1912.01412/) (2019)
- [Tree-to-tree Neural Networks for Program Translation](https://mlsanity.com/paper/1802.03691/) (2018)
- [Unsupervised Text Style Transfer using Language Models as Discriminators](https://mlsanity.com/paper/1805.11749/) (2018)
- [First Experiments with Neural Translation of Informal to Formal Mathematics](https://mlsanity.com/paper/1805.06502/) (2018)
- [A formal proof of the Kepler conjecture](https://mlsanity.com/paper/1501.02155/) (2015)
- [Exploration of Neural Machine Translation in Autoformalization of Mathematics in Mizar](https://mlsanity.com/paper/1912.02636/) (2019)
- [Towards String-to-Tree Neural Machine Translation](https://mlsanity.com/paper/1704.04743/) (2017)
- [Developing Corpus-based Translation Methods between Informal and Formal Mathematics: Project Description](https://mlsanity.com/paper/1405.3451/) (2014)
- [A Survey of Languages for Formalizing Mathematics](https://mlsanity.com/paper/2005.12876/) (2020)
- [Experiences from Exporting Major Proof Assistant Libraries](https://mlsanity.com/paper/2005.03089/) (2020)

## Built on by (most cited first, 1 of 1)

- [Towards Semantic Markup of Mathematical Documents via User Interaction](https://mlsanity.com/paper/2408.04656/) (2024)

## Abstract (arXiv)

We propose the task of disambiguating symbolic expressions in informal STEM documents in the form of LaTeX files - that is, determining their precise semantics and abstract syntax tree - as a neural machine translation task. We discuss the distinct challenges involved and present a dataset with roughly 33,000 entries. We evaluated several baseline models on this dataset, which failed to yield even syntactically valid LaTeX before overfitting. Consequently, we describe a methodology using a transformer language model pre-trained on sources obtained from arxiv.org, which yields promising results despite the small size of the dataset. We evaluate our model using a plurality of dedicated techniques, taking the syntax and semantics of symbolic expressions into account.