ACL 2021long11 citations
Can Sequence-to-Sequence Models Crack Substitution Ciphers?
Abstract
Decipherment of historical ciphers is a challenging problem. The language of the target plaintext might be unknown, and ciphertext can have a lot of noise. State-of-the-art decipherment methods use beam search and a neural language model to score candidate plaintext hypotheses for a given cipher, assuming the plaintext language is known. We propose an end-to-end multilingual model for solving simple substitution ciphers. We test our model on synthetic and real historical ciphers and show that our proposed method can decipher text without explicit language identification while still being robust to noise.
BibTeX
@inproceedings{aldarrab-may-2021-sequence,
title = "Can Sequence-to-Sequence Models Crack Substitution Ciphers?",
author = "Aldarrab, Nada and
May, Jonathan",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "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)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.561/",
doi = "10.18653/v1/2021.acl-long.561",
pages = "7226--7235"
}