论文标题
用神经语言模型解决历史词典代码
Solving Historical Dictionary Codes with a Neural Language Model
论文作者
论文摘要
我们通过构建解码晶格并使用神经语言模型来搜索该晶格来解决困难的基于单词的替代代码。我们将我们的方法应用于1700年代末和1800年代初的美国陆军将军詹姆斯·威尔金森（James Wilkinson）和西班牙王室代理商之间交换的一套包裹信，并从美国国会图书馆获得。我们能够正确地破译75.1％的密码令牌。
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.
