opus-mt-en-pie: English to Para-Indo-European

Translates English into Para-Indo-European, a speakable Proto-Indo-European.

Proto-Indo-European, the ancestor of most languages of Europe and much of South Asia, was never written down; linguists reconstruct it from its daughters and disagree on many details. Para-Indo-European follows one reconstruction and answers every open question with one stated choice, kept everywhere. That makes it a para-language: a variety that could plausibly have been spoken alongside the real one, not a claim about exactly what was said. Every choice, with its main alternative, is on the positions page.

It has reconstructed roots, eight cases, aspect instead of tense, a real mediopassive and verb-final word order, spelled in a plain orthography a phone keyboard can type (h x f for the three laryngeals, written and said between consonants too: pxtéer "father"; c j jh for the palatals, q v vh for the labiovelars, doubled vowels for length).

• Try it: the Space runs this model in the browser, with the engine, speech and a word-by-word breakdown.
• Learn it: the course on Linguardia, where each word's popup shows its root and whether it is attested, derived or coined.

Version 3: an audited lexicon and PIE sentence conventions

Every word marked attested was checked against LIV², Mallory & Adams, NIL and the branch dictionaries by one reviewer and every proposed change by a second: 287 forms corrected (consonant series, laryngeals, wrong roots for the sense), 98 thin attestations downgraded, 87 declensions and genders fixed. Neuter consonant stems, pronoun ablatives and numerals up to a million are now made by rule. The training sentences follow PIE rather than Germanic phrasing: "X has Y" is "to X is Y", yes/no questions keep the verb last, comparatives take the ablative, and neuter plural subjects take a singular verb. 4,000 numeral sentences were added.

Coinages are built only on PIE roots: "far-" words (phone, television, radio) use *kʷel- "far", spelled qél-, not Greek tēle: phone is qélwoqax "far-voice". Because the model writes word keys and the engine spells them, lexicon fixes like this reach the output without retraining.

Version 2: the model writes a grammar plan, the engine spells it

The model does not write finished words. It writes which word, in which case or person, in which order:

I'm drinking alcohol with my family.
-> my%ins.sg.n kin#ins.sg with alcohol#acc.sg drink@1sg.prs .
-> Hméyoh jénhesh kóm médhupaawrom píbfox.

The language's grammar engine turns the plan into words (engine/engine.js, with the lexicon in engine/engine-lex.json; a line-for-line port of the Python engine, identical on all 218,045 forms). So every word form is one the grammar actually makes, and a display can show exactly what each piece of a word is. A key the lexicon does not have (the model reaching past what it knows, e.g. skyscraper#acc.sg) cannot be spelled, which makes an unknown word visible instead of a confident wrong guess.

Plan syntax: word@person.mood[.mid] verb, word#case.number noun, word%[cmp.|sup.]case.number.gender adjective, word$case pronoun, N! numeral, a bare word is a particle or postposition, =qe attaches "and".

Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tok = AutoTokenizer.from_pretrained("linguardia/opus-mt-en-pie")
model = AutoModelForSeq2SeqLM.from_pretrained("linguardia/opus-mt-en-pie")
batch = tok([">>pie<< Perkunos struck the serpent with his stone."], return_tensors="pt")
plan = tok.batch_decode(model.generate(**batch, num_beams=5), skip_special_tokens=True)[0]
# 'perkwunos#nom.sg iz$gen stone#ins.sg serpent#acc.sg strike@3sg.aor .'

import * as Engine from './engine/engine.js'
Engine.init(await (await fetch('./engine/engine-lex.json')).json())
Engine.render(plan)   // 'Pérqunos tósyo xcmnéh hóvhim vhénet.'

The >>pie<< prefix is required: it is the target-language token added for this finetune.

Training data

Every target is a tagged analysis the engine compiles and validates, so the target side is correct by construction with respect to the language's own grammar and lexicon.

Version 2 adds about 2,700 lexemes so the language has a way to say nearly every word among the 10,000 most common in everyday English sentences: about 2,200 English synonyms were mapped onto existing words, and the rest got an attested PIE root, an ordinary PIE derivation, or a transparent compound on the coinage system (vehicles on wagon, buildings on house, devices on the instrument suffix, electric on power, distilled drink on fire: alcohol médhupaawrom "mead-fire"). It never borrows. Every root's consonant series was audited against the standard reconstructions.

None of the 2,000 course sentences used for evaluation appear in training.

Evaluation

On held-out sentences from the course, plans spelled by the engine, against references respelled with the audited lexicon and conventions:

Version 3 is the checkpoint with the lowest validation loss (step 3,000 of a 2-epoch run).

Limits

• Para-Indo-European is a reconstruction of a reconstruction. Treat output as a well-informed guess, not attested speech.
• Rare modern words outside the lexicon come out as an English key the engine cannot spell; coin them in the lexicon.
• Long or complex English (several clauses, rare idioms) degrades quickly; the training sentences are short.
• English articles, tense nuances and phrasal verbs are mapped onto PIE categories, so some distinctions are lost.

Licence

Base model Helsinki-NLP/opus-mt-en-mul, Apache-2.0. Training data, engine and lexicon by linguardia. This finetune: Apache-2.0.