A lot of people have been talking about machine translation in customer service. It would make replying to tickets more efficient, improve the customer experience, and even help companies expand to different countries without having to hire native agents.
However, most customer service leaders are skeptical about introducing this technology into their workflows—and for good reason.
For most people, Google Translate is the first thing that comes to mind upon mention of machine translation (MT). But would you really trust it to translate everything you need to tell your customers?
Probably not — considering mistakes like mistranslating the name of a Spanish food festival as “clitoris festival,” or identifying the phrase “Ooga Booga Wooga” as Somali. Nonetheless, this doesn’t make machine translation irrelevant to customer service. And here’s why.
Even if you have the best customer support agents at your disposal, their ability to serve clients has one obvious limitation: language. So, what are your options if you need to provide customer support in markets with different languages?
You can hire a bunch of native agents and train them (which is costly and time-consuming). Or you can automate translation (reducing costs and making your team more efficient).
Imagine if your French speaking agents could seamlessly communicate with Chinese customers in their native language (in this case, Mandarin). Wouldn’t it be great? Or, if you could distribute multilingual customer support tickets equally among team members, regardless of what languages they speak, during peak season? Wouldn’t it be the holy grail of operational efficiency?
The answer is yes, of course, it would. But there’s one significant detail that prevents most customer service managers from automating translation, and that is translation quality.
Machine translation quality — we’re gonna have to earn it
CS operational managers (as well as most people) perceive translation quality from a single point of view: it must be perfect. On the other hand, in rapidly expanding businesses, language is nothing more than just a tool and its quality should be fit for purpose.
So how do you make sure you have the highest quality translations without having to hire an international community of customer support agents to rival the Eurovision lineup?
Well, that’s what we’re working on at Unbabel. We combine the best of machine translation with a community of tens of thousands of bilingual editors who review and approve the translations.
And part of the reason why we involve humans in the process is because machine translation alone can’t yet deliver the quality we need. For machine translation to work, we need human translations to feed into the systems and train them. Once the system receives all the data, it starts to learn patterns and to produce better translations.
But what if humans weren’t involved in this process? Would machine translation be enough for customer service?
I doubt it. And let me tell you why.
At Unbabel, we have translated crazy amounts of customer service interactions for companies like Booking.com, EasyJet, Skyscanner, Under Armour, and King, and if there’s one thing we know, it’s that machines make mistakes (some of which are not so easy to spot).
Below are some of the most common mistakes made by machines in translations of customer service messages — mistakes that our community of editors have spotted and corrected.
1. Corrupted meaning — it’s a free-for-all
No company likes to give things away for free. Needless to say it’d be bad for business if your translations left customers thinking that you do.
Here’s an example of an actual translation which had to be reviewed and edited by our bilingual editors:
Source (English): You recently notified us of the possibility that copyrighted material was being made available through our website.
Machine translation (German): Sie haben uns vor Kurzem von der Überzeugung in Kenntnis gesetzt, dass urheberrechtlich geschütztes Material auf unserer Website kostenlos verfügbar ist. [You recently notified us of a belief that copyrighted material was being made available at no cost through our website.]
The problem with this is that the word “available” was translated into German as “available at no cost”.
2. MT peculiarities — where am I?
Some travelers learn to love the unexpected. But nobody wants to end up stranded in the wrong city because of a translation error.
Source (Russian): Наш хостел расположен в деревне Туришкино, которая находится в 60 км от Санкт-Петербурга.
[Our hostel is located in the village of Turishkino, which is 60 km away from St.Peterersburg]
Machine translation (English): Our hostel is located in village Tururushkaino, which is 60 km away from St.Peterersburg.
Since the neural machine translation system did not have the name of the village “Туришкино” in its lexical bank (to be fair, it’s a pretty rare word), it had to translate it into something else. Wrong translation, wrong city.
This may also happen when you convert units of length:
Source (English): If you live just 20 kilometres away from San Diego, you may consider driving to the Westfield Mission Valley mall and collecting it yourself.
Machine translation (French): Si vous habitez à seulement 20 milles de San Diego, vous pouvez envisager de vous rendre au centre commercial Westfield Mission Valley et de le récupérer vous-même.
3. MT hallucinations — the ghost of texts past
Sometimes machines see things that aren’t actually there, haunted as they are by the memory of translations in their database. We like to call this phenomenon MT hallucinations.
For instance, the machine may add unnecessary words to the translation, as in the example below:
Source (English): The contract is understandable.
Machine translation (French): Le contrat est compréhensible, veuillez nous appeler dès que possible.
[The contract is understandable, please call us as soon as possible.]
In this case, what happened was that the MT system referred to previous translation examples and generated an extra clause which did not appear in the source text: “please call us as soon as possible”.
But the MT system can also do the opposite and erase parts of the message:
Source (English): It looks like it took a while for the subscription to be marked inactive but it is cancelled now.
Machine translation (German): Es scheint, dass es eine Weile gedauert hat, bis das Abonnement als inaktiv markiert wurde.
[It looks like it took a while for the subscription to be marked inactive.]
In this case, the whole chunk of text “but it is canceled now” was not translated into German.
4. Register and tone of voice — what did you call me?
Languages have their own set of rules — that’s part of the reason why translation is so difficult. But when it comes to adapting register and tone of voice in customer service, you need to be extra careful with how you address people.
Here’s a simple example of the incorrect use of a pronoun in machine translation:
Source (English): Make sure you have the latest operating system on your device
Machine translation (German): Stellst du sicher, dass du das neueste Betriebssystem auf deinem Gerät hast
[Make sure you [informal] have the latest operating system on your [informal] device]
The customer usually defines the choice of register. However, the use of the inappropriate register (like the use of informal “du” instead of formal “Sie” in this example) can be a real threat when communicating with customers, who may see it as impolite.
5. Overtranslations — is that an off-brand?
Some words are not supposed to be translated, like a company’s name or a person’s name. But machines don’t always know that.
So, overtranslations such as this one are quite common:
Source (English): I checked with the seller and as long as it not am Rapid Cheetah product, it is fine.
Machine translation (German): Ich habe mit dem Verkäufer überprüft und solange es kein Schnellesgeparden produkt ist, ist es in Ordnung.
[I checked with the seller and as long as it not a rapid cheetah product, it is fine.]*
Here, the brand’s name, “Rapid Cheetah,” is given as a literal translation in German. Sure, it’s funny, but it can also be confusing or even off-putting to customers.
6. Inconsistent or incorrect use of terminology — too many words
One word may have different translations and you need to know exactly which one to use when communicating with your customers. And when things go wrong, it can look weird:
Source (English): Packages 1 and 2 both charge a monthly fee, as these have additional features to Package 1.
Machine translation (Dutch): Pakketten 1 en 2 vragen elk een maandelijks bedrag, omdat deze extra functies hebben voor Pakket 1.
[Abonnements 1 and 2 both charge a monthly fee, as these have additional features to Abonnement 1.]
In this example, the term “package” was required to be translated as “abonnement” and not “pakket“. I guess the MT system chose the wrong word.
In short, pure machine translation systems lack the “human touch” required for understanding cultural references and contextual differences. Today, however, MT combined with advanced, automated quality assurance and post-editing by humans, ensures translations that are sound, and sound good — and often delivered within 20 minutes.
This is a game-changer for customer service where it’s really not just a matter of quality but also translation speed. In a world where customers are not willing to wait more than 10 minutes to get their problems solved, attending to their needs in their native language on time is crucial. And here’s how machine translation can help.
Machine translation may not be at the end of its road, but it has come a long way toward meeting critical business needs. And this is just the beginning.
The post The good, the bad and the ugly of machine translation for customer service appeared first on Unbabel.