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”.
Abstract:
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
