  We solve difficult word-based substitution codes by constructing a decoding
lattice and searching that lattice with a neural language model. We apply our
method to a set of enciphered letters exchanged between US Army General James
Wilkinson and agents of the Spanish Crown in the late 1700s and early 1800s,
obtained from the US Library of Congress. We are able to decipher 75.1% of the
cipher-word tokens correctly.
