Is Machine Translation a threat or an opportunity?

Machine Translation (MT) is one of the most discussed topics in the world of translators at the moment (on par with collapsing fees). Most of the arguments revolve around either its usefulness or the threat it poses to the professional human translators. We briefly touched on it within a previous post but we would like to go a bit deeper here, and provide some ideas about making the most of MT within the current translation workflow.

What is MT?

Wikipedia tells us that Machine translation is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another (warning, this Wikipedia page is quite outdated, as evidenced by the tiny mention of the neural network-based approach). Within the world of translation, this means the automatic translation of a piece of text by software that analyzes the source, without human intervention. This is different (and complementary) from systems based on translation memories.

This post is not a technical essay on the inner workings of MT, and we are not going to explain how the translation is actually done. Many approaches were proposed and over the years, with increasing success. However, the paradigmatic change happened – as in many other domains – when people started to use “deep learning“, i.e. using cascades of artificial neural networks trained on a huge amount of data (for more technical information you can read Google’s Neural Machine Translation (NMT) paper in arXiv). Suddenly, one could actually copy-paste an e-mail or a webpage in a translation tool and understand what it was about. Sure, the result is not perfect. Let’s be frank, it is often quite bad and sometimes funny. But it is understandable, and more or less looks like what a human with an intermediate level in a foreign language could produce when translating a text about a topic they know nothing about. And the spelling and grammar are better than many of the e-mails, text messages and Facebook posts we are all daily subjected to. The latest massive improvement came with the DeepL system, training the network using the Linguee database of existing translations.

How does the professional translation world work?

In order to understand the disruption brought by MT, it is useful to recapitulate how a large part of the professional translation is organized. There are exceptions to what we describe below, fields of translation where people interact differently, such as companies with embedded translation offices, authors dealing directly with their translators, etc. We are not concerned by these, although MT has presumably a large impact there as well.

First of all, there are three different jobs involved in the production of a translated document: 1) Translation per se; 2) Editing (for which the source document is needed), where one checks that the translation is accurate, all the requirements followed (e.g. no translation of person and product names), and 3) Proofreading (for which the source document is not needed), where one checks spelling, grammar, punctuation, etc. (this is the so-called TEP workflow).

Typically, when someone, the end client, is in need of a translation, they will either contact a translation company or will post a job advert on one of the many possible websites, either non-specialised – such as Upwork or Freelancer.com – or specialized in translation – such as TranslatorsBase or TranslatorCafé. The companies can be real translation companies, performing in-house translation, or agencies, outsourcing the work. In most cases though, some outsourcing will be involved since very few companies have enough employees to cover all language pairs and expertise in all fields. Such outsourcing will be done through the company’s own network of freelancers, via professional platforms such as ProZ or using the sites mentioned above. Now, sometimes, the outsourcing process does not stop here, and a cascade of subcontracting unfolds, with decreasing fees at each step of the ladder. Unfortunately, as the fees decrease, so does the quality of the translation. This is why a revision step is put in place by the outsourcers. This can be just a proofreading exercise, fixing spelling, punctuation and the occasional grammar issue. Or it can turn into a heavier editing task, correcting translation mistakes. In the case of an outsourcing cascade, this can effectively become a retranslation.

How is MT affecting the translation pipeline

Before the advent of NMT, MT produced a text so bad, that it took a professional translator longer to fix it than retranslating from scratch. A machine-translated text was also immediately obvious, even when compared to bad human translations. All that had now changed. The quality of the produced translations increased dramatically (at least in certain cases. We discuss this in the next section) and large amounts of text can be translated very very quickly. While the free online versions generally limit every single translation to a few thousand characters, one can extend that via APIs (with or without fees, see for instance the R package deeplr).

This triggered two consequences, one ethical, one unethical, but both unfortunate. The first consequence is that some agencies think they can stop outsourcing the human translation part of a job and only pay for the revision one. The second consequence is that some freelancers pretend to translate themselves while they just use MT and a superficial revision. To be honest, in the latter case we are generally at the bottom of the subcontracting cascade, and the human translation would be quite bad anyway. In both cases, the result is a text that requires edition rather than proofreading. In the first case, agencies are honest and openly admit the fact, offering jobs of Machine Translation Post Editing (MTPE). But, and this is the crux of the problem, in both cases, the rate offered is at the level of proofreading rather than editing.

Improved MT also brought another change to the working practices of a professional translator. Many translators use Computer Aided Translation tools. Typically, such a tool divides the source text into segments, that are translated separately. Those tools now provide access to MT engines to provide suggestions for segment translations, as an alternative to Translation Memories (even if one could argue that DeepL is somehow linked to an uber TM, in the form of the Linguee database).

The luddites

Understandably, the world of professional translation has been shaken by the sudden rise of NMT. In a couple of years, what was seen as a promising field of research became a game changer. The reaction in such situations is always the same. It broadly follows the Five Stages of Grief. Because of the past history of the field, most translators went through the denial period. Many are still stuck there. Using the cases where MT performs badly – albeit not worse than a casual translator not doing their homework – as evidence, such people reject its relevance entirely. A portion of the community moved on the bargaining phase (trying to avoid or compete with MT), and some are even in the acceptance phase. However, a very vocal part of the community is currently in the anger phase. In some sense, they are similar to the Luddites who refused industrialization for fear that it would suppress their jobs. However, since they cannot break the MT engines, they turn their anger towards the translators using it. They are mistaken in exactly the same way as the 19th-century Luddites. They fear that the change of paradigm will remove the need for skilled workers and replace them with unskilled cheap ones. While exactly the opposite will happen, as it did a few centuries ago when automation created highly skilled jobs and removed the lowly paid manual ones. The segment of the translation community that will be the most affected by MT is the domain of non-technical, low quality, translation, while the skills of specialised human translators will be more recognized than they were when lost is an ocean of mediocre translators. Which brings us to the strengths and weaknesses of MT.

How good is MT?

So, machine translation improved tremendously, but how good is it for practical purposes? Sure, we all came across funny translations, and we can all do with a good laugh. However, for simple texts, the result is OK. DeepL’s translations to French of the following sentences is almost perfect: “The sky is grey. It is likely to rain”, “Postman Pat’s truck is red”, “The Luddites were a secret oath-based organization”, “Jeremy Corbyn is the leader of the labour party”. In the case of the first sentence, DeepL actually chooses a correct but suboptimal translation (Il est probable qu’il pleuve). However, it picks the right one (Il va probablement pleuvoir) if we add a double quote at the end, which reveals one of the problems specific to its approach, that is oversensitivity to local context in existing translations. That said, Google Translate always picks the suboptimal solution.

This suggests a range of situations were MT could be used: Everyday’s discourse, children stories, factual descriptions, and news. What have those situations in common? The language is simple, and must be understood by everyone. These are “layperson translations”.

Now, by contrast, MT fails with highly specialised and technical documents, when the language requires a pre-existing particular knowledge from the reader, not shared by the entire population. Why is that? Because MT cannot cope with several situations, including the following:

  • When a word has several widely different meanings, and the source text does not use the most frequent one. For instance, in the ecclesiastic world, the French word “coule” designs a garment worn by monks. Now, MT will always believe “coule” is a verb meaning either some liquid moving from up to down, or something that get submerged by water, The proposed translations will be flow, run, pour, sink, cast (if what is flowing is metal or cement), stream, trickle, or even founder. It will never be cowl.
  • Not the same word or expression in different languages. Here we find the famous “il pleut comme vache qui pisse” translated into “it rains cats and dogs”. Same underlying meaning, totally different expression. In general, all such imaged expressions tend to be translated literally by MT, resulting in completely meaningless sentences.
  • Meronymy/Holonymy, that is when the word used in a language represents part of the thing which the equivalent word in another language represents. I am not talking about synecdoche here, that is a stylistic figure which uses the part for the whole or the other way around.
  • Hyponymy and hypernymy, that is when a word in a language represents a generalization of the thing represented by the word in the other language. For instance, “seagull” is a layperson English word representing a subset of the family Laridae. In French, there is no such layperson term. Instead one will use either “goéland” representing the genus Larus which are big birds, or “mouette” representing several genera of the subfamily Larinae which are small birds. MT has not way to know which one the author of the source text meant (even if the previous sentence clarified the issue).
  • Complex relationships. In English, the temporal bone is separated into parts coming from different embryological origins (the squamous, petrous and tympanic bones). In French, the temporal bone is separated into regions of the adult structure, the “écaille”, “rocher”, and “mastoid”. It is impossible to translate one into the other. One has to reconstruct the entire description.
  • Context-dependent translation. MT typically focused on a word and its immediate surrounding. For instance, a human translator will understand that in the following sentences “La fille regarda les jouets qu’on lui avait offert. Son ballon était bleu et son vélo rouge”, the ball and the bike are the girl’s ones. But MT cannot determined that. Both GT and DeepL translate it into: “The girl looked at the toys that had been given to her. His balloon was blue and his bike red.” (which is a great example of unintended but real sexism by the way).

I am certain, there are other areas where MT performs unevenly or badly (for instance when it comes to household names, slang, etc.)

Among the other issues presented by MT are two problems that mirror each other. Since the MT engines have no memory of the entire text, the same word can be translated differently in different parts. Sometimes it does not matter, as in “stream” and “trickle” in the example above. Sometimes it does, if we get sometimes “stream” and sometimes “cowl”! Conversely, because MT engines were built on a given training set, they tend to produce texts that are boring in terms of vocabulary and “robotic” in terms of style. To be fair, this is much less of a problem with Deepl than with GT. Also, the problem is worse with translation memories, so MT might even be an improvement here.

Two ways of using MT in professional translation

At the heart of the debate and disagreement around MT in the professional translation setting lies a lack of clarity on the way it is and/or should be used. At the moment, there are two very different ways of using MT for translation:
1) using MT to perform the whole translation, and ask third parties to review the results.
2) using MT as part of a piece of the toolkit to perform translations, for instance, to provide starting points or alternatives for segments, in parallel to translation memories.

Many agencies, or publishers, think MT is ready for 1), while it is not. Let’s be really really clear here: MT is not the key to automatically – and cheaply – translate corpora of texts, either articles or books, etc.

Furthermore, reviewing translations performed that way is extremely difficult. It is by no mean a proofreading exercise, but rather an editing exercise. We had to edit large texts which comprised parts translated by MT and parts translated by a human who clearly was not a native of the target language. Both types were difficult to edit. However, there was one crucial difference: While the human-translated parts presented a horrendous style and many grammatical mistakes, the MT parts presented WRONG translations. In most cases, this is much worse. For instance, in the biomedical domain, tiny misunderstanding might lead to dreadful consequences.

Conversely, many professional translators think or claim that MT is not ready for 2), and cut themselves from a very useful tool. We wholeheartedly adopted 2). We think there is much improvement needed, and it is possible (see below). We believe translators, like any professionals, need to take control of their tools. When a farmer works out their field, they use various technologies. But one rarely sees some third parties, completely unaware of what was done to the ground and how it was done, to come and evaluate the work. We think MT should be used by translators, not blindly, but in a controlled manner. Then, we will be able to learn from it, but also to help it grow to become an even more useful tool.

How to use MT efficiently

  • Use MT on a segment per segment basis rather than for the whole text (the definition of what makes a segment is let to the imagination or the preferences of the reader/translator).
  • Never accepts a proposed translation blindly. Check all the important words, as well as tenses and accords.
  • Make full use of the alternatives provided for instance by DeepL. The proposed choice is statistical, but often the right or more accurate one is within the first 3-5 alternatives.
  • Once a significant chunk of text is translated, re-read in its entirety to make the style more homogeneous and reduce repetitions. To be fair, this is not specific for MT, and should always be done.
  • back-translate the text from the target to the source language, in order to spot possible ambiguities or mistranslations.

What do you think? Are you using Machine Translation at the moment? Which systems? How?

10 errors to avoid when starting as a translator

Many people start in the translation business without a corresponding professional training. This is absolutely fine, and it is in fact a good way of using one’s language skills acquired either during a professional activity or a travelling life. However, as amateurs, they probably all tend to make the same mistakes. Here we list a few of them.

1) Believing that a translation job is just … translating

A translation job is much more than converting a text from a source language to a target language. Glossaries and a bit of grammar polishing would almost be sufficient for that. However, a translator must convey the “content” of the source document. That involves of course translating the words. But it also, and foremost, involves producing a text that carries the same message. And to do so requires to understand what the text is about, in details and with all its subtleties. This is why all translators have their specialities, and although most translators can do an OK job with any text in their paired languages, they really excel only within a few niches.

Conveying the proper meaning is sometimes at odd with keeping to a strict translation of the words themselves. Depending on the domain covered, one wants to massage the text to make it more readable and respect the form of the source text. With the exception of legal documents – where one must absolutely stick to the original, even if the result seems quite heavy – some sentence restructuring and expression switching is needed to make the result more palatable, and also truly equivalent in the target language. Finally, in the artistic domain, one wants to respect the style of the original, terse or verbose, dull or vivid, mainstream or abstruse. Lovecraft did not write like Stephen King despite hovering in the same literature space.

2) Starting the translation immediately

In order to translate a text accurately, we cannot start the work straight away. We must read the entire text beforehand, to make sure we understand what it is about, have an idea of the specialized knowledge we might need to acquire, and what was the goal of the authors. Such a preliminary read will only marginally increase the time spent on a text. Or at least it should, otherwise we are probably not spending enough time on the job! Reading a 100 000 words book before starting the translation might seem daunting, but the required time is still far less than what we will spend accurately translating those 100 000 words. And the gain down the line in terms of translation speed and accuracy largely makes up for the extra effort. During this initial read, we should make notes of anything we do not immediately get, any word or expression we did not come across in the past, and make sure we do fully understand it.

3) Trusting machine translation

Machine translation has seen astounding progress in the past few years. Software such as the Google Neural Machine Translation and (even more) DeepL , really transformed the activity to a point that, in many cases, the result really sounds like it has been produced by a native speaker, but is also better than a translation made by a casual translator, i.e. someone who would make most of the errors listed here … (By the way, this makes even more pathetic the ridiculous translations used in some places such as Stansted airport. It beggars belief that nowadays people produced voice announcements that barely make sense, and even check-in machines that speak some nonsense languages using random words assembled in sentences with no grammar whatsoever).

However, machine translation is still mostly good for straight texts, without nuance, technical jargon, and stylistic oddities. It is still too much based on word for word translation, or translation of short segments. This often results in wrong choices in case of homonyms in the source language, wrong split of propositions in long sentences, lots of repetitions etc. Also, machines seem to ignore basic life facts, such as only female give birth. So the translation of “They gave birth to their babies” is invariablyIls ont donné naissance à leurs bébés” and not “Elles ont donné naissance à leurs bébés”. More disturbingly, when we want to translate “he ate his date”, instead of “il a mangé sa date”, Google Translate provides “Il a mangé son rendez-vous” and DeepL even decides to add up slang to the delightful “Il a mangé son rencard“. Not very vegan.

That said, machine translation is generally a good feeder for Computer Assisted Translation, which brings us to the next mistake.

4) Blindly trusting the segment-based text proposed by our CAT software

Computer Assisted Translation speeds up translation massively. It saves all the time spent translating and typing trivial pieces of text such as “the red car”, “his name was Joe” and “the sky was gray and it was likely to rain”. However, CAT cannot be trusted blindly. CAT translation is based on segmentation. The text is split in small parts, containing one or a few sentences. The software then suggest translations for each segment.

Firstly, some of those translations might come from machine translation, e.g. Google Translate or DeepL. Thus, see point 3. But very often the translations come from Translation Memories. Translation memories come with their own problems. Sometimes the translations proposed are plainly wrong, with missing words or wrong sentence parsing (resulting in wrong adjective associations for adjectives or verbs for instance). Another important issue is error propagation. If a segment was badly translated once, and this translation was recorded in TMs, it will be proposed in future translations.

A very important issue is the fact that the translations proposed for a segment is done purely on this segment, independently of the content of other segments of the text. There is rarely enough context in a single segment to discriminate between different meanings of a term.

Finally, the segmentation largely follows the punctuation in the source language. Depending on the translation, for instance in literary works where one needs to keep a style and rhythm, the optimal split might be different in the target language. Fortunately, CAT tools offer segment split/merge facilities.

5) Assuming the source document is right

This is a thorny issue. The basic position is that the source language document is correct, and we need to faithfully translate it. But this is not necessarily the case. Everyone makes mistakes, even the most thorough writers. Some mistakes are easy to spot and to correct, and many should not affect the translation, such as unambiguous spelling errors. However, others will be much harder to detect. For instance, words with similar pronunciations in English (the ubiquitous “complimentary” for “complementary”, “add” for “had”, “your” for “you’re” or the dreadful “of” for “have”), or absence of accents (or incorrect ones) in French, will lead to completely wrong translations. In many case, the context will provide a quick answer, but sometimes a bit more brain juice is needed. We should always double check that we understood the text correctly, and that our chosen translation is the only one.

Finally, horror, some “errors” are made on purpose, for stylistic reasons. In the case of a novel or a play, wrong grammar or vocabulary might be part of the plot or a defining feature of a character. In that case, we probably must provide a translation that contain a correct equivalent of the initial erroneous text …

6) Forgetting to double check the punctuation

OK, that might actually be a specific version of the previous error. Translators are linguists, and as all linguists, we are in love with punctuation (aren’t we?). Is there anything that beats the Oxford comma as a favorite topic for conversation? (except perhaps split infinitives) Surprisingly enough, this is not the case of every person, or even every writer. Punctuation can be a life saver in the case of very long and complex sentences. It can also be a killer in case it is absent, or, heaven forbid, wrongly placed. For instance, observe the following bit of text:
“an off-flavour affecting negatively the positive fruity and floral wine aromas known as Brett character.”

What is the “Brett character”? (enlightened disciples of Bacchus, lower your hand). Is it the positive fruity and floral wine aromas? Or is it the off-flavour? It is, in fact, the latter, a metallic taste given by some yeast (from the genus Brettanomyces). Of course, the answer would be much clearer if the source sentence was:

“an off-flavour affecting negatively the positive fruity and floral wine aromas, known as “Brett character”.”

But let’s not add punctuation to Guillaume Appolinaire’s poetry, and keep Le Pont Mirabeau free of punctuation. Actually, the following translation of La Tour Eiffel might be one of the truest poetry translation ever, respecting the meaning, the style, and the shape.

7) Not paying attention to the mainstream use bias

This error is often a side-effect of using CAT tools with TMs or MT. The proposed translations will often rely on the most frequent meaning of a term, and its most frequent translation. This is not necessarily the meaning which is the right one, or the best one, for the current source document.

Sometimes, this is just irritating. For instance, in a literary text talking about “petits détours”, CAT will keep suggesting “small detours”. While this is correct, it does not fully convey the idea carried by “petits” here. It is too bland too quantitative, and “little detours” is the best translation, as shown here, here and here.

However, the mistake can be more severe. Google Translate tells us the story of a dreadful mum, “She put a bow in her daughter’s hair” being translated into “Elle a mis un arc dans les cheveux de sa fille”. That must have hurt terribly. As was the case for the poor lad who “entered a ball” and ended up “entré dans un ballon” (GT) or even “entré dans une balle” (DeepL), instead of “entré dans un bal”. Not much room to dance there. Sometimes, the mainstream use is actually overridden by the politically correct one, and the saucy “he was nibbling at her tit” is translated into “il mordillait sa mésange”. Except if we are talking about a cat, that is a disturbing image instead of a titillating one. While those examples were a bit joky, some cases are harder to spot. Someone who planted “Indian flags” in their garden will almost always end up in French exhibiting their nationalism rather than their love of irises.

In some cases, the various meanings have similar frequencies in daily use, and different tools provide alternative suggestions. DeepL will suits plumbers providing “installer un compteur” for “To set up a counter”, while Google Translate will lean towards merchants with “mettre en place un comptoir“.

8) Trying to stick 100% to the words of the source text

The true meaning of a word goes beyond its definition in a thesaurus. They carry different weight in different languages. The rude word meaning faeces is used as an interjection in almost every language. However, the level of rudeness is different in all western European countries, and sometimes choosing another rude word of the adequate level is better (no, we will not provide examples). And of course, there are very few cases where anyone should translate “it rains cats and dogs” into “il pleut des chats et des chiens”. One should always translate it into “il pleut comme vache qui pisse” (it rains as if a cow was pissing). While the new image is no so much better, at least no animal is hurt.

9) Trying to stick 100% to the structure of the source text

Trying to reproduce absolutely the structure of the source document is very tempting and encouraged by the segmentation process of CAT tools. However, this is lazy. English sentences are known to be shorter than French ones. Therefore, translating a sentence from the latter language might require several in the former. Let’s not speak of German where an entire sentence might end up in a single word! As usual, first comes the meaning, then the rhythm, then the style. Not only this requires to merge/split sentences, it might also require swapping propositions or sentences.

10) Not reading back the complete resulting translation

Last but not least, we should never forget to re-read attentively the entire translation. In the profession, proofreading is often mentioned as an activity disconnected from translation. But no translation work should be considered complete without a proofreading step! This is even more important if CAT software were used. They are known to promote “sentence salads”, where heterogeneous texts, in style and vocabulary, are caused by using the memory of many previous translations.

What about yourself? Which mistake did you make when learning how to become an accurate and efficient translator?