A generative model for the Mycenaean Linear B script and its application in infilling text from ancient tablets

We present a generative neural language model for the most ancient proven stage of the Greek language, the Mycenaean Greek attributed by the Linear B script. To capture the statistical structure of the Mycenaean documents we present a Bidirectional Recurrent Neural Network and compare it to the traditionally used n-grams. The model is used to supplement the damaged parts of the Mycenaean texts, namely the incomplete, to a greater or lesser extent, words, which are typically discovered on partially damaged clay tablets. We verify our method experimentally using ground - truth, then we demonstrate our results on real cases and compare with experts' opinions. We also present a methodology to augment our dataset, which turns out to improve our results.