  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 informed by patterns in language change documented in
historical linguistics. The model utilizes an expressive sequence-to-sequence
model to capture character-level correspondences between cognates. To
effectively train the model in an unsupervised manner, we innovate the training
procedure by formalizing it as a minimum-cost flow problem. When applied to the
decipherment of Ugaritic, we achieve a 5.5% absolute improvement over
state-of-the-art results. We also report the first automatic results in
deciphering Linear B, a syllabic language related to ancient Greek, where our
model correctly translates 67.3% of cognates.
