Machine Translation
Translating between languages automatically — the task that invented modern NLP, where fluency arrived long before reliability.
When not to use it
- For anything you'd sign. Fluent errors are invisible if you can't read the source. Contracts, medical, legal — human, with review.
- On low-resource languages, unchecked. Quality tracks corpus size, and most of the world's languages have small corpora.
- Sentence by sentence, for a document. Pronouns lose referents, terminology drifts, register wanders.
- When the text is culturally loaded. Idiom, humour, register, connotation. Those don't map, and the system will produce something fluent regardless.
Reach for something else instead
- Post-editing — machine draft, human fix. Faster than from scratch, and how professional translation now works.
- Human translation — for anything published or consequential.
- Controlled source language — write the original to be translatable: short sentences, no idiom, consistent terms.
- Terminology-constrained MT — force specific term translations. Underused and effective for technical content.
Sources & further reading
- Bahdanau, Cho & Bengio (2014), Neural Machine Translation by Jointly Learning to Align and Translate — attention, invented for translation, and the ancestor of the transformer.
- Papineni et al. (2002), BLEU: a Method for Automatic Evaluation of Machine Translation — the metric everyone knows is inadequate and still uses.
- Läubli, Sennrich & Volk (2018), Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation — how the parity claims dissolved under better evaluation.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Trusting fluency. A wrong translation reads exactly as well as a right one.
- Judging quality on BLEU. It rewards word overlap and penalises legitimate paraphrase.
- Assuming quality transfers across language pairs. It tracks corpus size, and that varies enormously.
- Translating documents sentence-by-sentence and losing everything that spans sentences.
- Not noticing that gender and formality were invented for you, usually along stereotype lines.
At a glance
FieldApplied AI
Historical roleinvented attention, hence transformers
Signature failurefluent when wrong
MetricBLEU, known inadequate
Quality trackscorpus size, not technology
Right shape for productionpost-editing
DifficultyBeginner
Flashcards for this concept
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Often compared with
Machine translation vs. post-editing — raw output is for gisting; machine draft plus human fix is for publishing. The second is how professional translation actually works now.