  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.
