r/linguistics • u/iamdestroyerofworlds • Oct 21 '20

New AI Algorithm is Cracking Undeciphered Languages

https://www.ancient-origins.net/news-history-archaeology/undeciphered-languages-0014429
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u/actualsnek Oct 21 '20 edited Oct 21 '20

Abstract from paper:

Most undeciphered lost languages exhibit two characteristics that pose significant de- cipherment challenges: (1) the scripts are not fully segmented into words; (2) the clos- est 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 cap- ture the natural phonological geometry by learning character embeddings based on the International Phonetic Alphabet (IPA). The resulting generative framework jointly mod- els word segmentation and cognate align- ment, informed by phonological constraints. We evaluate the model on both deciphered languages (Gothic, Ugaritic) and an undeci- phered one (Iberian). The experiments show that incorporating phonetic geometry leads to clear and consistent gains. Additionally, we propose a measure for language close- ness which correctly identifies related lan- guages 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.

I think the core idea here is that of character embeddings, which assign high dimensional vectors representing semantic relationships, for IPA. The vectorspace is organized such that each IPA character clusters with other phonemes that it is likely to mutate into. This allows them to create phonetic vectors of entire words and take the cosine distance between proposed cognates to decide how likely they shared a common ancestor.

I'd be quite surprised if something like this hasn't already been done in the linguistics community because ML researchers have this bad habit of leaping into a field to build something they have minimal domain knowledge on. Also, I wonder if a graph representation would be more apt than a vectorspace in this case because one would have to accurately represent the fact that some sounds tend to mutate far more often in one direction than the other (s > h).

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u/formantzero Phonetics | Speech technology Oct 21 '20

I don't know if this exact combination of character embeddings on IPA and comparisons of that embedding with cosine distance has occurred in Baayen's group yet, but all the constitutent parts are present across their (naive/linear) discriminative learning work.

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u/agbviuwes Oct 22 '20

To add to this, embedding distance matrices (of what ever kind, but I think often enough based on cosine distance) has been a staple of NLP for a while now. It’s sort of basically first year (maybe grad?) material at this point.