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Can Sequence-to-Sequence Models Crack Substitution Ciphers?
Nada Aldarrab, Jonathan May, 2021. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers).
Trains a character-level transformer to decode simple substitution ciphers directly, without searching for a key, and tests it on unseen keys and on several plaintext languages.
What it's used for here
The cryptanalysis survey lists it among neural decipherment methods, and the inductive-methods survey includes it in its references.
Cited by on this site 2 records
2 methods surveys
SRCCryptanalysis, inductive methods
Cite it
Aldarrab, N., & May, J. (2021). Can Sequence-to-Sequence Models Crack Substitution Ciphers?. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). https://doi.org/10.18653/v1/2021.acl-long.561
@article{aldarrab2021can,
author = {Aldarrab, Nada and May, Jonathan},
title = {Can Sequence-to-Sequence Models Crack Substitution Ciphers?},
journal = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
year = {2021},
doi = {10.18653/v1/2021.acl-long.561},
url = {https://doi.org/10.18653/v1/2021.acl-long.561}
}TY - JOUR TI - Can Sequence-to-Sequence Models Crack Substitution Ciphers? AU - Aldarrab, Nada AU - May, Jonathan PY - 2021 JO - Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) DO - 10.18653/v1/2021.acl-long.561 UR - https://doi.org/10.18653/v1/2021.acl-long.561 ER -
Aldarrab, Nada and Jonathan May. “Can Sequence-to-Sequence Models Crack Substitution Ciphers?.” Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (2021). https://doi.org/10.18653/v1/2021.acl-long.561.