Deciphering Undersegmented Ancient Scripts Using Phonetic Prior
Published
2022-01-04
Jiaming Luo
,
Frederik Hartmann
,
Enrico Santus
,
Yuan Cao
,
Regina Barzilay
Jiaming Luo
Massachusetts Institute of Technology
Frederik Hartmann
University of Konstanz
Enrico Santus
Bayer AG
Yuan Cao
Google
Regina Barzilay
Massachusetts Institute of Technology
Abstract
Most undeciphered lost languages exhibit two characteristics that pose significant decipherment challenges: (1) the scripts are not fully segmented into words; (2) the closest known language is not determined. We propose a decipherment model that handles both of these challenges by building on rich linguistic constraints reflecting consistent patterns in historical sound change. We capture the natural phonological geometry by learning character embeddings based on the International Phonetic Alphabet (IPA). The resulting generative framework jointly models word segmentation and cognate alignment, informed by phonological constraints. We evaluate the model on both deciphered languages (Gothic, Ugaritic) and an undeciphered one (Iberian). The experiments show that incorporating phonetic geometry leads to clear and consistent gains. Additionally, we propose a measure for language closeness which correctly identifies related languages for Gothic and Ugaritic. For Iberian, the method does not show strong evidence supporting Basque as a related language, concurring with the favored position by the current scholarship.
Article at MIT Press
Presented at EACL 2021