On July 26th 2:30pm in TASC1 9204 W, Nishant will defend his Masters thesis on the topic “DECIPHERMENT OF SUBSTITUTION CIPHERS USING NEURAL LANGUAGE MODELS”.
The decipherment of homophonic substitution ciphers using language models (LMs) is a well-studied task in Natural Language Processing (NLP). Previous work in this topic score short local spans of possible plaintext decipherments using n-gram LMs. The most widely used technique is the use of a beam search with n-gram LMs proposed by Nuhn et al. (2013). We propose a new approach on decipherment using a beam search algorithm that scores the entire candidate plaintext at each step with a neural LM. We augment beam search with a novel rest cost estimation that exploits the predictive power of a neural LM. This work, to our knowledge, is the first to use a large pretrained neural language model for decipherment. Our neural decipherment approach outperforms the state-of-the-art n-gram based methods on many different ciphers. On challenging ciphers such as the Beale cipher, our system reports significantly lower error rates with much smaller beam sizes.
M.Sc. Examining Committee:
Dr. Anoop Sarkar, Senior Supervisor Dr. Fred Popowich, Supervisor Dr. David Campbell, Examiner Dr. Keval Vora, Chair