Solving Historical Dictionary Codes with a Neural Language Model
October 09, 2020 ยท Declared Dead ยท ๐ Conference on Empirical Methods in Natural Language Processing
"No code URL or promise found in abstract"
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Authors
Christopher Chu, Raphael Valenti, Kevin Knight
arXiv ID
2010.04746
Category
cs.CL: Computation & Language
Citations
0
Venue
Conference on Empirical Methods in Natural Language Processing
Last Checked
6 months ago
Abstract
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.
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