Translation

What professional translation is, and how it differs from word for word

Professional translation means the reader of the target language gets what the reader of the original got: the same meaning, the same tone, the same register and the same function. Swapping word for word delivers only one of those four and drops the rest on the way.

  • Lesson 1 of 6
  • Beginner
  • Free, no signup

What crosses this bridge and what does not

Words are the lightest load on this bridge. The other three are heavier, and they are the ones usually left behind.

The source text

written by someone else

MeaningToneRegisterFunction

The target reader

the same takeaway, another language

Some things do not cross at all: a pun, the metre of a poem, a legal term with no matching institution in the target language. The professional decision here is not how to get everything across, it is which one you sacrifice, and telling the owner of the text which one.

Last checked: Facts and tool names in this lesson are re-checked against their sources on this date.

What translation actually carries across

Translation is rewriting a text in another language so that the new reader takes away what the reader of the original took away. Words are one of the things that move across, and as it happens the least important of them.

Four things have to arrive. Meaning, which is the most obvious and the least trouble. Tone, meaning whether the writer is serious or playful, distant or close. Register, meaning whether the text is formal, spoken or technical. And function, meaning what the text is supposed to do: sell, warn, teach, or simply stand as a record.

One short example is enough. In a message between friends, "see you later" becomes something casual; in an official letter it never becomes a ceremonial formula. Both readings defend themselves on meaning alone, and one of them, in the wrong place, makes the text ridiculous. Being wrong is not the problem here. Being out of place is.

That is where the line between professional translation and word for word translation gets drawn. Word for word delivers the meaning and leaves the other three to luck. A professional decides all four on purpose, and when one of them has to be sacrificed, knows which one and why.

Two glass vessels of different shapes holding the same volume of green liquid, rims lit red and blue

What has to be settled before the first sentence

A beginner opens the file and starts at the first sentence. A professional has four answers before that, and will not start without them.

  • Who reads this and how much do they already know. A technical guide for an engineer and the same guide for a customer are two different texts, even when the source is one file.
  • What should the reader be able to do afterwards. If they are meant to click a button, the sentence has to be short; if they are meant to sign a document, it has to be exact even when that makes it heavy.
  • Where the register sits. Decide it once for the whole text and write it down somewhere you can see, because register is the thing that slides without permission on page two.
  • What must not be translated at all. Brand names, product names, a legal term with no matching institution in the target language, a code fragment, a file name, a unit that is standard. This list is the shortest part of the job and the one that causes the most argument.

Take that fourth item seriously. Whenever a translation falls apart halfway, it is usually because someone translated a product name, or rendered one term differently in chapter three. Writing the list before you start takes ten minutes and prevents a full rewrite.

How translation and localisation differ

Translation moves the text. Localisation changes the text so it fits where it lands.

Things localisation touches and translation must not: units, currency and dates, an example that only means something to the source reader, the shape of a phone number and an address, the order of given name and family name, the direction of the page, and anything the destination country requires by law that the source does not. These are not translations. They are decisions, and the owner of the text has to know about them.

Most sites that call themselves multilingual have only been translated, and that is where it stops. The result is a text that is correct and reads foreign: a price in dollars for a reader who thinks in another currency, or a form asking for the family name first from someone who has always written their given name first.

An example from this site. The Arabic version of this section is not the Persian page in Arabic letters: its digits are the Arabic-Indic set rather than the Persian one, and its letters are the Arabic yeh and kaf rather than the Persian pair. Those forms are wrong in Persian and right in Arabic, and the other way round. Had we only translated, the Arabic would have been correct and slightly off for an Arabic reader. Google publishes its own guidance on localised versions of a site, and it says the same thing: each version has to be built for the reader of that version, not merely rendered into it.

Translation and localisation, two separate jobs on one text

Both are needed and they are not the same. Ordering one and expecting the other is the most common misunderstanding in this trade.

Translation: it moves the text

  • Keeps the meaning, the tone and the register
  • Follows the structure and order of the source
  • Leaves examples, numbers and units alone
  • One translator does it

Localisation: it settles the text in

  • Moves units, currency and dates to the destination
  • Swaps an unfamiliar example for a familiar one
  • Changes the shape of forms, names and addresses
  • It is the text owner's decision, not the translator's

Localisation costs more because it needs decisions, and a translator does not make those alone. If the budget only stretches to one, translate the text and confine localisation to the pages money passes through.

Where Persian and English catch on each other

Every language pair has its own catches, and this pair has two large ones.

First, politeness and distance. Persian has an informal and a formal second person and a whole system of verb forms and suffixes that tunes the distance between speaker and listener. English has one "you" and does the same job through word choice and sentence shape. So coming from English into Persian forces a decision the source never made, and getting it wrong is immediately audible to the reader. Most translated texts that read as off on Iranian sites broke exactly there, not on meaning.

Second, sentence length. Persian tolerates long chains and a long sentence sits naturally in it. English punishes the same chain. A good Persian to English translation therefore tends to break sentences up, and a good English to Persian one sometimes joins two into one.

Now a number, from this section's own data. Each of the 148 lessons here is written in four languages, and we counted the Persian and English of every text field pair by pair: 7,152 pairs where both sides ran to at least eight words, on 9 September 2026. The result was not what we expected. By word count Persian came out only slightly longer than English; by character count it came out 13 percent shorter. Persian takes more words, and each of its words is shorter.

The average is not the point, though. In 82 percent of the pairs the two sides stayed within twenty percent of each other, in 12 percent Persian ran more than twenty percent longer, and in 6 percent more than twenty percent shorter. Roughly one field in six swings hard one way. A layout built around the average breaks on exactly that sixth. Space for a button or a heading has to be designed for the spread, not for the mean.

One honest caveat: these texts are not translations, they are written separately in all four languages. So the figure above describes parallel writing, and on a real translation, which is obliged to follow the structure of its source, the spread gets wider rather than narrower.

How close Persian and English come out in length

The difference in word length between the Persian and the English of the same field.

share of 7,152 text pairs
  1. Within twenty percent of each other 82 percent these are the ones that never cause trouble
  2. Persian more than twenty percent longer 12 percent this is where buttons and headings go to two lines
  3. Persian more than twenty percent shorter 6 percent the cards that end up uneven beside each other

Our own measurement over the 148 lessons of this section, 9 September 2026. These texts are not translations but separate writing in four languages, so on a real translation, obliged to follow the structure of its source, the spread runs wider than this.

Where a professional translator says no

Three places, and all three are marks of professionalism rather than weakness.

The first is direction. You write into the language you are strongest in and read from the language you are merely competent in. Someone whose first language is Persian and whose English is good will usually translate better into Persian than out of it, and that is not a flaw. Turning down a Persian to English job when the text is going to be published is the right call. No model vendor publishes separate numbers for the two directions either, so not even the tools will tell you which direction of yours is weaker.

The second is the field. Medical, legal and financial texts carry terms whose correct equivalent is known only to someone who has worked in that field. One displaced word in a contract inverts the meaning. If you have a text of that kind and you are not its translator, go to someone who has worked in the field; our own translation service is organised along exactly that split.

The third gets said less often: when the source sentence is itself wrong. A translator does not quietly fix it. They translate it, then tell the owner of the text where the problem is. Fixing it quietly means the reader of the target text reads something the author never wrote, and when the two versions are eventually laid side by side, it is the translator who has to explain.

The three together say one thing: a translator does not own the text, they hold it in trust. Wherever that gets forgotten, the work slides from translation into rewriting, and nobody notices until it is late.

The fast path, with AI

There is a habit experienced translators have and almost no course teaches: before translating, they read the source once to find the traps rather than to understand it. That pass is exactly what a good language model does, on one hard condition: it may not translate anything and it may not propose equivalents. It produces a list, nothing else.

  1. Paste the source only. If you put your own draft beside it, the model starts grading your work and stops reading the source. This pass is not about the translation yet.
  2. Take a fast, cheap model. This job is mechanical and needs no judgement, so a light model of the Flash class is enough, and the current pick is listed in this site's <a class="text-link" href="/en/ai/">AI section</a>. Keep the expensive model for the translation itself.
  3. Run the recipe below. Its main constraint is that the model may not translate and may not propose equivalents; that single constraint is what turns the output from a bad translation into a useful list.
  4. Cross the output down. Delete everything you already knew. What is left is the real work list for this text, and it is usually shorter than you feared and somewhere other than you guessed.
  5. Turn item four of the list by hand into a do-not-translate list and show it to the owner of the text before you start. The model finds the candidates; whether a product name gets translated is their decision, not yours and not the model's.

Copy-ready recipe

This source text is going to be translated from {source language} into {target language}:

{source text}

Do not translate anything and do not propose any equivalents. Give me only a list:

1) Every specialist term, and whether it has more than one common equivalent in the target language.
2) Every sentence whose pronoun or referent is ambiguous and cannot be translated without guessing.
3) Every idiom, figure of speech or wordplay whose form cannot be carried across.
4) Every name, brand, file name, unit or code fragment that probably should not be translated.
5) Every sentence longer than {word ceiling} words.
6) Every place where the register changes, quoting the sentence the change happens in.

Give the output as a table, with the paragraph number from the source. If a category is empty, leave it empty and add nothing of your own.

Before you trust the output: This list is a start and it is not complete. The model does not find a trap it has no name for, and cultural reference, meaning the place where the source reader knows something the target reader does not, is what falls out most often. Set one rule and keep it: do not ask the model for a translation in the same call. The moment it starts producing target text, its list stops being about the source.

AI in this kind of work

Our position in this lesson is simple: AI is excellent at reading the source, good at producing a first draft, and bad at the four decisions named in the second section. Those four depend on something the model does not have, namely knowledge of the reader and your relationship with the owner of the text. A model that does not know whether this text is for a customer or an engineer takes its register from the average of the internet and hands it to you.

Tools that actually help

  • Claude It holds the "do not translate, only list" constraint well, which is exactly what the trap scan needs. Anthropic's own page says Iran is not on its list of supported countries.
  • Gemini It reads Persian itself, so a Persian source does not have to go through English first. Google's own page says the app works in over 230 countries and territories and more than 70 languages, and Iran is not on that list.
  • Cohere The only vendor in our reference that calls one of its models a translation model outright and lists its 23 languages one by one, with Persian, Arabic, Turkish and English all on that list. Cohere's own commercial agreement writes Iran by name into its Restricted Location definition, so there is no official way to buy it from Iran.

Where it backfires

Two risks, and the first is the one you see in translated text without knowing why. A model does not preserve register unless you tell it to; if you do not, it pulls the text towards the average and the result is a formal text gone slightly casual, or a casual one gone slightly bureaucratic. We have seen this in our own texts and we claim no number or study for it; the remedy is simple, state the register in the same call and then read the first and last passages yourself. The second risk concerns the source itself. Contracts, medical files and financial documents are confidential, and whether your conversation is used for training depends on the plan and the settings; Anthropic states its rule for consumer products on its own page, and Google does the same for the Gemini apps. If the text is signed or names people, you need the owner's permission before you paste, and that is a legal decision rather than a software setting.

Sources: Anthropic: supported countries and regions Google: where Gemini Apps are available Cohere: commercial SaaS agreement (Restricted Locations) Cohere: models, with the Command A Translate language list Anthropic: is my data used for model training Google: how Gemini Apps data is used

Where this advice stops

This lesson is about decisions, not about language. It does not teach you English, Arabic or Turkish and offers no shortcut to learning them; without command of the language, everything written here is only a glossary. Two areas are also deliberately left out. Sworn translation, which is not a skill but a permit and the business of a licensed office. And literary translation, which breaks these rules on purpose: in poetry and fiction, fidelity to tone sometimes outranks fidelity to meaning, and everything said in the fifth section about holding a text in trust takes another shape there. The numbers in the fourth section were measured on the texts of this site, and another language pair or another kind of text produces a different number.

From our own work

The marketplace on this site holds a live example of item four in the second section. The options in its listing form are 36 Persian strings, each of which is two things at once: a label a user reads, and a key stored verbatim in the database. The English, Arabic and Turkish version of every option is built from that key, and the key itself is never changed. Three of those 36 do not even conform to this site's own writing rules and have been left wrong on purpose, because correcting one of them would orphan every listing still holding the old value. The first thing any translator or editor wants to do on seeing that form is tidy up exactly those three. That is what a do-not-translate list exists for, and whether a string belongs on it has nothing to do with whether its spelling is correct.

Real follow-up questions

Is translating from Persian into English harder, or the other way round?

The right question is not which direction is harder but which language you write in. Whichever direction ends in your stronger language comes out easier and better, and for most Iranian translators that means English into Persian. Translation quality is not symmetrical, and no model vendor publishes separate numbers for the two directions either, so if a text is going to be published and its direction runs towards your weaker language, you need a native reviewer.

Does a translator have to be a specialist in the subject?

Not for a general text, yes for a text where a mistake costs something. The border is simple: if one wrong term can lead to a wrong decision, as in a medical text or a contract, the translator has to have worked in that field rather than merely own its glossary. The specialist translation lesson of this path is about exactly that border.

For a website, do I translate page by page or the whole site at once?

Page by page, but with one shared term list written before the first page. The problem with a multilingual site is usually not the quality of each page but the mismatch between pages: one term rendered one way on the product page and another way in the guide. The next lesson of this path explains why the tools do not take a whole document either, for exactly the same reason.