Solving Historical Dictionary Codes with a Neural Language Model (2020.emnlp-main)
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| Challenge: | a dictionary-based substitution code is common, but no automatic decipherment algorithms exist. |
| Approach: | They propose a decoding lattice and a neural language model to solve word-based substitution codes . they apply their method to letters exchanged between general James Wilkinson and agents of the Spanish Crown . |
| Outcome: | The proposed method decrypts letters written by general James Wilkinson and agents of the Spanish Crown in the late 1700s and early 1800s using a neural language model. |
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| Challenge: | Current methods for deciphering historical ciphers use beam search and a neural language model . but, this approach assumes that the target plaintext language is known . |
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| Challenge: | Existing methods for deciphering homophonic substitution ciphers use pre-trained neural LMs. |
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| Challenge: | Existing methods for deciphering historical substitution ciphers are difficult to crack . cipheries that are not segmented are still difficult to deciphere . |
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| Challenge: | This tutorial introduces brain-language model alignment and recent advances in brain-informed fine-tuning and brain-based fine-caching with language models. |
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Simon Suster, Pieter Fivez, Pietro Totis, Angelika Kimmig, Jesse Davis, Luc de Raedt, Walter Daelemans
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| Challenge: | Graph Rewriting is a mathematical formalism that can be used to describe rule-based transformations on linguistic structures. |
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