  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.
