Learning a dead language's dictionary from aligned tablets, with no dictionary ever supplied.
Sumerian is one of the oldest written languages on earth, and it is dead: no native speakers, just clay tablets. Can a machine learn to read it, at least a first pass, the way modern translation models are built, and can it learn its dictionary without ever being given one?
The MTAAC / CDLI parallel corpus: 32,578 aligned Sumerian and English phrases from real cuneiform tablets, administrative and literary, in the transliteration Assyriologists use rather than raw cuneiform. The corpus authors are candid that it is noisy, so the project is scoped and framed the same honest way.
A hybrid that extends the standard NLP pipeline (text to sequences, padding, embeddings, seq2seq). Layer one, live now: a dictionary learned by IBM Model 1 word alignment, which discovers that udu means sheep and ki means from purely from which words keep travelling together. Layer two, next: an LSTM encoder-decoder that reads whole lines in context.
With no dictionary supplied, alignment alone recovers correct readings for hundreds of Sumerian words and glosses formulaic tablet lines sensibly. It also surfaces the hard part honestly: Sumerian homophones like gu (ox, talent, thread) trip a context-free gloss, which is exactly what the neural layer is built to fix. Type a line and watch it read, word by word, with confidence and alternate readings.