MIT researchers teamed up with Google Brain to leverage deep learning in deciphering ancient languages. The key deep learning methods were utilized to quickly complete simple tasks at scale.
Paper: Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B
Author: Jiaming Luo (CSAIL, MIT), Yuan Cao (Google Brain), Regina Barzilay (CSAIL, MIT)
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.