Discovering the linear writing order of a two-dimensional ancient hieroglyphic script

This paper demonstrates how machine learning methods can be applied to deal with a real-world decipherment problem where very little background knowledge is available. The goal is to discover the linear order of a two-dimensional ancient script, Hieroglyphic Luwian. This paper records a complete decipherment process including encoding, modeling, parameter learning, optimization, and evaluation. The experiment shows that the proposed approach is general enough to recover the linear order of various manually generated two-dimensional scripts without needing to know in advance what language they represent and how the two-dimensional scripts were generated. Since the proposed method does not require domain specific knowledge, it can be applied not only to language problems but also order discovery tasks in other domains such as biology and chemistry.

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