Disambiguating Symbolic Expressions in Informal Documents

Dennis Müller; Cezary Kaliszyk

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arXiv · 2101.11716

Disambiguating Symbolic Expressions in Informal Documents

Dennis Müller·Cezary Kaliszyk

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Abstract

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.

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Dennis Müller, Cezary Kaliszyk. 2021-01-25. Disambiguating Symbolic Expressions in Informal Documents. https://arxiv.org/abs/2101.11716

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