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

Decipherment as Regression: Solving Historical Substitution Ciphers by Learning Symbol Recurrence Relations

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.

**Organizers:** 
Atul Kr. Ojha, DSI, University of Galway & Panlingua Language Processing LLP
Chao-Hong Liu, Potamu Research Ltd
Ekaterina Vylomova, University of Melbourne, Australia
Jade Abbott, Retro Rabbit
Jonathan Washington, Swarthmore College
Nathaniel Oco, National University (Philippines)
Tommi A Pirinen, UiT The Arctic University of Norway, Tromsø
Valentin Malykh, Huawei Noah’s Ark lab and Kazan Federal University
Varvara Logacheva, Skolkovo Institute of Science and Technology
Xiaobing Zhao, Minzu University of China For more details, please visit our [workshop website](https://sites.google.com/view/loresmt/)

The Sixth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2023)

workshop paper

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

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EACL 2023

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

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Nishant Kambhatla

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Decipherment as Regression: Solving Historical Substitution Ciphers by Learning Symbol Recurrence Relations

Decipherment as Regression: Solving Historical Substitution Ciphers by Learning Symbol Recurrence Relations

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