Type transliterated Sumerian and get a word-by-word English reading. The dictionary behind it was not hand-written, it was learned by aligning 32,578 Sumerian and English phrases from real cuneiform tablets. This is the first, honest layer of a translator, a neural model is the next.
This is a first-pass gloss, not a scholarly translation. It reads transliterated
Sumerian (Latin letters in the ATF style scholars use, not cuneiform pictures) using the corpus
conventions: hyphens between signs (lugal-e), determinatives in braces
({d}nanna, lagasz{ki}), and sz for the "sh" sound. It has no
grammar and no word order, it labels each word with its most likely meaning. Unknown words show in
red. Sumerian is ambiguous (one reading can mean several things, and
grammar words often fall back to a weak default like "the"), so treat this as a helper for a human,
exactly how the researchers who built the data intend it.
1(disz) or plain digits become
[number]. Diacritics (š, ḫ) are accepted and folded to ATF.Nobody typed in that udu means "sheep." The model saw tens of thousands of short phrase
pairs and used IBM Model 1, a classic word-alignment algorithm: it starts by assuming every
English word could translate every Sumerian word, then repeatedly rewards pairs that keep co-occurring.
After ten passes, udu has locked onto "sheep," ki onto "from," lugal
onto "king," kiszib3 onto "seal." It is the same idea as counting which words travel
together, made precise.
The honest failure it exposes: Sumerian is full of ambiguity. The same reading can mean several things, and grammatical particles carry little meaning on their own, so the model falls back to a weak "the." Seeing that is the point, it is exactly where the next layer, a neural sequence-to-sequence model that reads a whole line in context, earns its keep.
That neural model is the next build, and it extends the same NLP pipeline this project already uses: text to integer sequences, padding to a uniform length, an embedding layer, then an LSTM encoder-decoder.