Solving Historical Dictionary Codes with a Neural Language Model

October 09, 2020 ยท Declared Dead ยท ๐Ÿ› Conference on Empirical Methods in Natural Language Processing

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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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