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- [en-fr.md](https://md.mova.elamur.ai/free/en/docs/languages/translation/context/en-fr/) — English and French: renderings by context
- [en-uk.md](https://md.mova.elamur.ai/free/en/docs/languages/translation/context/en-uk/) — English and Ukrainian: renderings by context
- [fr-uk.md](https://md.mova.elamur.ai/free/en/docs/languages/translation/context/fr-uk/) — French and Ukrainian: renderings by context

# Context-dependent renderings: English, French, Ukrainian

Most words and many constructions have **several valid renderings**, and which one is right depends on the context. This folder documents those choices for the three pairs of the translation manuals, in both directions: [English and French: renderings by context](https://md.mova.elamur.ai/free/en/docs/languages/translation/context/en-fr/), [English and Ukrainian: renderings by context](https://md.mova.elamur.ai/free/en/docs/languages/translation/context/en-uk/), [French and Ukrainian: renderings by context](https://md.mova.elamur.ai/free/en/docs/languages/translation/context/fr-uk/), with the machine-readable lists [en-fr.tsv](https://md.mova.elamur.ai/free/en/docs/languages/translation/context/), [en-uk.tsv](https://md.mova.elamur.ai/free/en/docs/languages/translation/context/), [fr-uk.tsv](https://md.mova.elamur.ai/free/en/docs/languages/translation/context/). It is the companion of the [false-friend lists](https://md.mova.elamur.ai/free/en/docs/languages/translation/false-friends/): a false friend is a rendering that is wrong because it only looks right; a context entry is a choice among renderings that are each right somewhere. A partial false friend (*control* / *contrôler*, *питання* / *question*) usually has entries in both.

## 1. The model

A rendering is chosen by a small set of coordinates, the same ones the general manual uses for register ([general.md](https://md.mova.elamur.ai/free/en/docs/languages/translation/general/) §3):

> **sense × register coordinate (language + variety + style of speech) × domain × collocation × text type × reader → preferred rendering**

- **Sense.** The source word's meaning in this sentence: *issue* "problem", "edition", "emission of shares"; *справа* "matter", "case", "deed", "the right side". The sense comes from the sentence, the paragraph and the world (who acts, on what).
- **Register coordinate.** The target's language, its variety (France or Quebec French; standard Ukrainian or surzhyk in a character's speech) and its style of speech (official, neutral written, colloquial, slang, a song, a house style). *Get* is *obtenir* in a regulation and *avoir* or *choper* in a chat.
- **Domain.** Law, EU, economy, medicine, IT, science, news, military: each fixes terms (*case law* is *jurisprudence*; *case* in medicine is *cas*, in law *affaire*, *справа*).
- **Collocation and syntactic frame.** The words around it: *raise an issue* / *soulever une question*; *address a problem* / *s'attaquer à un problème*; *звернутися до суду* / *saisir le tribunal*. The frame decides function words too: *should* in a recommendation, in a condition, in a law.
- **Text type.** A regulation, a contract, a news report, a manual, a novel's dialogue, a subtitle, a song.
- **Reader.** The specialist, the general public, a child; and the relation of speaker and addressee, which decides *tu/vous* and *ти/ви/Ви* and the forms of address that English does not mark.

Each row of a list is one source word or construction **in one context** (`context_feature`: `domain: …`, `register: …`, `collocate: …`, `frame: …`, `text type: …`, `reader: …`), with the preferred rendering (`tgt_preferred`), the acceptable alternatives (`tgt_alternatives`), what is wrong in that context (`avoid`), a verified corpus example where one exists and a note with a constructed ✗ rendering and the ✓ one. A polysemous word therefore has several rows.

The literature describes the same task as **lexical selection**: one source concept, several target variants, and the context decides which is most appropriate, which native speakers can label and models can be tested on *(barua-2024-language-models-disambiguate-lexical-choices)*. Sentence-level translation is not enough for some of these choices: in a study of English–Russian subtitles, sentences that were good alone were wrong together in 7% of pairs, mostly through deixis (the T–V distinction and the speaker's or addressee's gender), ellipsis and lexical cohesion *(voita-2019-when-good-translation-is-wrong)*. The T–V choice cannot be read from English at all; it has to come from outside the sentence, as a side constraint in a model *(sennrich-2016-controlling-politeness-neural-machine-translation)* or, in a human translation, from the brief and the relation of the characters.

## 2. Deciding by measured usage

The lists are measured with the same tool as the false friends, [`parallel choices`](https://md.mova.elamur.ai/free/en/docs/tools/parallel/). A row's `src_match` names the source word, `ctx_match` (optional) a context the source sentence must also contain (a collocate, a preposition, a frame word), `good_match` the preferred rendering and `bad_match` the one to avoid. For each row the tool counts the sentence pairs with the source word in that context (`n_src`), and among them those with the preferred rendering (`n_good`) and those with the avoided one (`n_bad`), per corpus too. So *issue* + *raise* in Europarl shows how often *soulever une question*, *soulever un problème* or ✗ *soulever une issue* appear.

What the counts can and cannot say:

- **Co-occurrence, not alignment.** A rendering in the same sentence pair is evidence, not proof, that it translates the word. Read the rows before concluding.
- **Context by collocate only.** `ctx_match` conditions on a word in the source sentence; register, text type and reader are given by the corpus itself (Europarl and DGT are EU institutional language; Tatoeba short everyday sentences, often with several translations in different registers and with *ти* and *ви* versions; the Bibles old literary language). A context that no corpus represents (slang, songs, IT manuals in Ukrainian) is described, not measured, and the entry says so.
- **Living corpora** (<lang>, described in its README: news, web, Wikipedia, parliamentary and, for Ukrainian, the Brown-UK sample) and the Wikipedia counts of the one-sided manuals measure the target side alone: which of two renderings the target language uses more in a register. They are the check for the `register` rows; they were not used for the counts in these lists.
- **Two runs, same bytes.** The tool is deterministic.

Rows are sorted by `n_src` (`frequency_rank` 1 = the most frequent source word in its context), so the choices met most often come first.

## 3. How Mova will pick a variant

Mova does not translate French or Ukrainian today ([translation README](https://md.mova.elamur.ai/free/en/docs/languages/translation/)). The lists are written so that it can:

1. **Parse** the source: the Universal Dependencies tree gives the word's lemma, part of speech and its dependents, which match `ctx_match` and the frame (*raise* → obj → *issue*).
2. **Pragmatics and frames** give the sense and the participants: an *issue* that is debated is a question, an *issue* of a journal is a number; a *case* with a court is a legal case. The frame also gives the speaker–addressee relation for *tu/vous*, *ти/ви*.
3. **The register coordinate** comes from the text's metadata and the brief (the document's house style, the reader), not from the sentence.
4. A row whose features all hold gives its `tgt_preferred`; several rows are ranked by how specific they are (a collocate beats a domain, a domain beats the default) and by their measured `n_good / n_src`. The `avoid` column becomes a check on the output, like the false-friend check, with a negative control: a row must stay silent outside its context.

None of these steps runs on these lists yet; the parse exists for English and, through mapped treebank tags, for French; Ukrainian has no Mova parser baseline.

## 4. Examples we make

Where no corpus row shows a context (most legal, IT and register rows for Ukrainian), and for popular words where a better example teaches more, `parallel tr-run` has the Codex teachers write our own source sentence for the context and its translation through `mova-teacher`: **Terra** for `difficulty` = `plain`, **Sol** for `hard:<reason>`. The gates are those of the [false-friend lists](https://md.mova.elamur.ai/free/en/docs/languages/translation/false-friends/#3-examples-we-make) (the translation must use a preferred or acceptable rendering and never the avoided one; a second call judges the hard ones; Mova's parse of the English and French sides is recorded as findings). Accepted pairs get ids `ours-tr:<pair>:<n>`, live in `<pair>.ours.tsv` and become the row's example (`example_status` = `constructed`). On 2026-11-06, 345 context rows were queued: 335 got a pair, 10 were rejected by the gates and stay `needs_example`. The queue is [`languages/translation/examples-queue.tsv`](https://md.mova.elamur.ai/free/en/docs/languages/translation/).

## 5. Files and columns

| file | what it is | licence |
| --- | --- | --- |
| `<pair>.md` | the pair manual: prose per domain and per kind of choice, then the rows, both directions | Elamur Cosmic License |
| `<pair>.tsv` | the list, one row per word in one context | Elamur Cosmic License: our own, hand-made data |
| `<pair>.examples.tsv` | the corpus rows quoted as examples | the corpus licences (Tatoeba CC BY 2.0 FR with authors; Europarl and DGT-TM EU re-use with attribution; the Bibles and Gutenberg public domain) |

List columns: `src_lang`, `src_lemma_or_pattern`, `context_feature`, `tgt_preferred`, `tgt_alternatives`, `avoid`, `example_id`, `note`, then `domain` (the false-friend domains plus `function`, `register`, `collocation`), `src_match`, `ctx_match`, `good_match`, `bad_match`, `difficulty`, and the measured `n_src`, `n_good`, `n_bad`, `by_corpus`, `example_ok`, `frequency_rank`, `example_status`. The rebuild commands are those of the [false-friend README](https://md.mova.elamur.ai/free/en/docs/languages/translation/false-friends/) §5, with the context lists.

**Counts (2026-11-06).**

| pair | rows | from the first language | from the second | with a verified corpus example | needing an example | marked hard |
| --- | --- | --- | --- | --- | --- | --- |
| [en-fr](https://md.mova.elamur.ai/free/en/docs/languages/translation/context/en-fr/) | 495 | 317 (en) | 178 (fr) | 487 | 8 | 190 |
| [en-uk](https://md.mova.elamur.ai/free/en/docs/languages/translation/context/en-uk/) | 462 | 337 (en) | 125 (uk) | 368 | 94 | 153 |
| [fr-uk](https://md.mova.elamur.ai/free/en/docs/languages/translation/context/fr-uk/) | 537 | 295 (fr) | 242 (uk) | 294 | 243 | 189 |
| total | 1 494 |  |  | 1 149 | 345 | 532 |

Every picked example was read by eye by the list's author on 2026-11-06; each manual's *Data and method* section gives how many picks were replaced or removed. Every example id was then checked again by a separate scan of the corpus files: all ids exist, with the same two texts.

## 6. Limits

- The counts condition on one collocate at most and say nothing about register beyond the corpus's own; many register, slang and song rows are judgements, marked as unmeasured.
- The Ukrainian corpora are small; the French–Ukrainian Tatoeba set is 34 310 pairs.
- The lists are a selection of the most frequent and most instructive choices, not a dictionary.
- Mova's selection from parse and frames is designed here, not built.

*The Elamur team*

- [next → False friends: English, French, Ukrainian](https://md.mova.elamur.ai/free/en/docs/languages/translation/false-friends/)
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