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        <title>Decipherment as Regression: Solving Historical Substitution Ciphers by Learning Symbol Recurrence Relations</title>
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    <name type="personal">
        <namePart type="given">Nishant</namePart>
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    <name type="personal">
        <namePart type="given">Logan</namePart>
        <namePart type="family">Born</namePart>
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    <name type="personal">
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            <namePart type="given">Isabelle</namePart>
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    <abstract>Solving substitution ciphers involves mapping sequences of cipher symbols to fluent text in a target language. This has conventionally been formulated as a search problem, to find the decipherment key using a character-level language model to constrain the search space. This work instead frames decipherment as a sequence prediction task, using a Transformer-based causal language model to learn recurrences between characters in a ciphertext. We introduce a novel technique for transcribing arbitrary substitution ciphers into a common recurrence encoding. By leveraging this technique, we (i) create a large synthetic dataset of homophonic ciphers using random keys, and (ii) train a decipherment model that predicts the plaintext sequence given a recurrence-encoded ciphertext. Our method achieves strong results on synthetic 1:1 and homophonic ciphers, and cracks several real historic homophonic ciphers. Our analysis shows that the model learns recurrence relations between cipher symbols and recovers decipherment keys in its self-attention.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-eacl.160</identifier>
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%0 Conference Proceedings
%T Decipherment as Regression: Solving Historical Substitution Ciphers by Learning Symbol Recurrence Relations
%A Kambhatla, Nishant
%A Born, Logan
%A Sarkar, Anoop
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F kambhatla-etal-2023-decipherment
%X Solving substitution ciphers involves mapping sequences of cipher symbols to fluent text in a target language. This has conventionally been formulated as a search problem, to find the decipherment key using a character-level language model to constrain the search space. This work instead frames decipherment as a sequence prediction task, using a Transformer-based causal language model to learn recurrences between characters in a ciphertext. We introduce a novel technique for transcribing arbitrary substitution ciphers into a common recurrence encoding. By leveraging this technique, we (i) create a large synthetic dataset of homophonic ciphers using random keys, and (ii) train a decipherment model that predicts the plaintext sequence given a recurrence-encoded ciphertext. Our method achieves strong results on synthetic 1:1 and homophonic ciphers, and cracks several real historic homophonic ciphers. Our analysis shows that the model learns recurrence relations between cipher symbols and recovers decipherment keys in its self-attention.
%R 10.18653/v1/2023.findings-eacl.160
%U https://aclanthology.org/2023.findings-eacl.160
%U https://doi.org/10.18653/v1/2023.findings-eacl.160
%P 2136-2152

Markdown (Informal)

[Decipherment as Regression: Solving Historical Substitution Ciphers by Learning Symbol Recurrence Relations](https://aclanthology.org/2023.findings-eacl.160) (Kambhatla et al., Findings 2023)

• Decipherment as Regression: Solving Historical Substitution Ciphers by Learning Symbol Recurrence Relations (Kambhatla et al., Findings 2023)

ACL

• Nishant Kambhatla, Logan Born, and Anoop Sarkar. 2023. Decipherment as Regression: Solving Historical Substitution Ciphers by Learning Symbol Recurrence Relations. In Findings of the Association for Computational Linguistics: EACL 2023, pages 2136–2152, Dubrovnik, Croatia. Association for Computational Linguistics.