Debunking machine translation myths

Machine translation (MT) is everywhere and it seems that everybody’s using it—from translating a restaurant menu in a foreign country to translating business content for localization. Despite its popularity and how much we now know about it, there are still a lot of myths floating around about how it works.

Is it here to replace human translators? Does it output only funny “word salads”? Does it leak your confidential inputs?

The truth? It’s somewhere in the middle.

In this article, we’re going to bust some of the biggest machine translation myths. From debunking the idea that all MT systems are the same, to exploring why it’s not always as cheap or accurate as it seems, we’ll give you the lowdown on what MT can really do.

Myth 1: MT can take over human translation

There’s no denying that MT is getting better and better by the year. However, it does not (and it probably never will) replace human translators. While fast and efficient, MT often fail to grasp nuances, cultural context, idiomatic expressions, and specialized terminology. These systems excel in straightforward tasks but struggle with complex texts that require a deep understanding of context.

This is why it’s still recommended you use a combination of machine and human translation, known as Machine Translation Post-Editing (MTPE), as a way to enhance accuracy and avoid mistranslations. Humans review machine outputs to correct errors and adapt them to context, so that you get high-quality results for business-critical or creative content.

Myth 2: MT works equally well in any domain

Just because a MT system did a good job translating a certain type of content, doesn’t mean it will perform just as well in other domains. Sure, it can do a pretty decent job in general context, but if you want to translate domain-specific content like legal or medical text, this requires extensive customization and terminology databases. You’ll have to train the neural machine translation (NMT) model on domain-specific data to improve accuracy. Without such preparation, the translation quality for complex content can suffer.

Myth 3: MT is always inaccurate

Yes, there are many people who choose to trust MT with any type of translation, but there are also plenty of others that refrain from using MT because these systems are often criticized for inaccuracies. While it’s definitely not flawless, MT excels in many scenarios, particularly when applied to straightforward or repetitive tasks. With human oversight and domain-specific customization, MT can be used for quality translations too.

Myth 4: MT is always cheaper

At first glance, it’s not hard to believe that MT is always the cost-effective alternative to human translation. It’s true, it is often cheaper per word—or even free—compared to hiring professional translators. But despite its low initial cost, relying solely on MT can lead to hidden expenses.

We discussed how MT often struggles with certain type of content, and this could lead to mistranslations. Fixing bad translations through post-editing is an additional cost. Poorly translated text can take as much—or more—effort to correct than translating from scratch.

Myth 5: MT systems are not flexible enough

Earlier translation systems provided limited customization or visibility into their processes, making it hard to understand why certain translations were chosen. Today’s MT systems, however, offer extensive customization capabilities. You can train models with your proprietary data, creating outputs tailored to industry-specific terminology or style.

Myth 6: Your private data is never reused

One of the common machine translation myths is that using machine translation tools is entirely safe, and that any data inputted will remain private. While many providers implement strong security protocols, the reality is more nuanced.

It’s no secret that MT models, especially NMT, improve by being exposed to vast amounts of data. When users input text into free translation tools, that data can sometimes be stored and used for training purposes unless explicitly stated otherwise in the tool’s privacy policy.

Popular free tools like Google Translate or DeepL Free often come with terms that allow them to use non-confidential inputs for improving their systems. For example, Google explicitly states that user-provided data may be used to refine its services unless the user disables the sharing option in their settings.

Paid versions, like Cloud Translation API or DeepL Pro, typically come with enhanced privacy features. These include contractual guarantees that input data will not be retained or used for training purposes, making them suitable for sensitive business or legal documents.

Myth 7: All MT systems perform the same

Some believe MT systems are interchangeable and provide very similar results. That is not exactly true. Some systems perform better when it comes to fluency and idiomatic expressions but may offer a smaller range of languages, while others support a broader range of languages and are more versatile for general use. There are also enterprise solutions that generally provide extensive customization options.

The performance of MT systems varies significantly across different language pairs too. It’s also worth noting that MT systems trained for specific industries or domains perform better in their respective fields. Consequently, each system has its strengths, weaknesses, and optimal use cases. At the end of the day, it all depends on specific needs like language pair, text type, domain, and privacy considerations.

To conclude

Machine translation may not perfect, but it’s far more powerful and accurate than many people give it credit for. By busting these common myths—whether it’s about cost, accuracy, or data security—we hopefully gave you a better understanding of how MT fits into our world and how to use it most effectively.

The thing to remember is that MT is an incredibly useful tool when used in the right context, but it’s not a one-size-fits-all solution. If you’re looking for quick translations in a less specialized industry, it could work for you. For complex, nuanced, or highly sensitive materials, human oversight remains mandatory.