We tackle the problem of neural machine translation of mathematical formulae
between ambiguous presentation languages and unambiguous content languages.
Compared to neural machine translation on natural language, mathematical
formulae have a much smaller vocabulary and much longer sequences of symbols,
while their translation requires extreme precision to satisfy mathematical
information needs. In this work, we perform the tasks of translating from LaTeX
to Mathematica as well as from LaTeX to semantic LaTeX. While recurrent,
recursive, and transformer networks struggle with preserving all contained
information, we find that convolutional sequence-to-sequence networks achieve
95.1% and 90.7% exact matches, respectively.

本论文的主要研究方向是解决神经机器翻译数学公式的问题，特别是涉及到模糊表示语言和明确内容语言之间的翻译，论文采用卷积序列到序列网络来翻译 LaTeX 和 Mathematica，达到了 95.1% 和 90.7% 的准确匹配。

数学公式的神经机器翻译

Neural Machine Translation for Mathematical Formulae

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.

利用 Transformer 语言模型针对 LaTeX 文件中的符号表达式进行自然语言处理并进行语法与语义分析。