Most undeciphered lost languages exhibit two characteristics that pose significant
decipherment challenges:(1) the scripts are not fully segmented into words;(2) the closest …

In this paper we propose a novel neural approach for automatic decipherment of lost
languages. To compensate for the lack of strong supervision signal, our model design is …

Token embeddings, a mapping from discrete lexical symbols to continuous vectors, are at
the heart of any language model (LM). However, lexical symbol meanings can also be …

Algorithmic decipherment is a prime example of a truly unsupervised problem. The first step
in the decipherment process is the identification of the encrypted language. We propose …

Abstract Project Gutenberg, begun by Michael Hart in 1971, is an attempt to make public
domain electronic texts available to the public in an easily available and useable form. The …

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 …

Abstract Research in Artificial Intelligence has shown that machines can be programmed to
perform as well as, or even better than humans in specific tasks, such as playing Chess …

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 …

Solving substitution ciphers involves mapping sequences of cipher symbols to fluent text in a
target language. This has conventionally been formulated as a search problem, to find the …

Abstract Monte Carlo Tree Search (MCTS) is a general search algorithm that has improved
the state of the art for multiple games and optimization problems. Nested Rollout Policy …