  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.
