Machine Translation and Technical Documentation
Over time, Machine translation (MT) has changed dramatically. From the systems that required hours of computing time to produce a poor translation to the current neural machine translation (NMT) systems that can process the same content in mere seconds and with much more accuracy. In recent years, machine translation has become widespread, and now it can be considered an essential part of life for the technical editors and translation teams.
Neural machine translation (NMT) is typically software used to translate words from one language to another. Google Translate, Baidu Translate are well-known examples of NMT offered to the public via the Internet.
However, introducing machine translation always involves a lot of groundwork; it may take a few months until suitable processes are in place, and all the requirements are met. You can’t just start using MT and expect to get the same high-quality output as you would from human translations.
Not any type of text fits for machine translation. As a general rule, the less context and fewer cultural references in the source text, the more suitable it will be for MT. The translations will almost always contain errors and won’t sound like idiomatic human language — sometimes, the effect will be highly jarring.
At best, poor translations will make people laugh. They may damage a company’s image — after all, the translation is part of the product, and if that part of the product is poor, people’s opinion of the product as a whole will go down. Even more seriously, the customer might not be able to maintain a product properly and end up damaging it. But the absolute worst-case scenario is personal injury. Manufacturers are liable for poor-quality translations, and that goes for machine translation just as it does for human translation. With an even higher risk. So, machine translation should always be used with professional post-editing. MT without post-editing means you won’t meet the necessary standards. You don’t know what the engine will spit out, and neural engines in particular frequently leave bits out, add bits in and distort the content of texts. And you won’t notice those errors if you simply use the output as is.
That’s why it’s important to carry out a risk assessment before you begin using machine translation. A cyber privacy is also an issue here. Sometimes people are too careless when using machine translation. For example, non-native speakers who write operating instructions in English sometimes just copy a sentence or paragraph into Google Translate. But, the text might contain sensitive product information, and often it’s the kind of information they’re forbidden from disclosing to anyone as part of their employment contract. But that’s exactly what they’re doing when they use free MT engines. So you should be very careful.
Technical documentation, like operating instructions, are usually well-suited to MT. In terms of the language they use, they’re ideal: the phrasing is simple, and the aim of the text is simply to inform.
If you want to use machine translation for your texts, ideally you should think differently in terms of content creation for your technical documentation.
- There’s a golden rule for the language you use: keep it simple, clear and consistent.
- The more modular and repetitive it is, the better, especially when you remember how translation memories make it extremely easy to reuse texts.
- Make sure you spell everything correctly and phrase it sensibly, which means keeping sentences short — it’s been shown that the quality of MT outputs gets worse as sentences get longer.
- Use the active rather than the passive voice.
- Resist the temptation to use humour, unusual words, dialectal variations and abbreviations.
- It’s also better to use fewer pronouns and more definite articles.
That might sound obvious and straightforward, but in practice you need the discipline and self-reflection to rigorously implement these aspects and produce a text that will make the translation process go more smoothly. Authoring systems are worth considering, as they can help make texts more suitable for MT engines.
Machines can’t yet replace humans when it comes to communication. However, the future of neural machine translation is very bright, and its capabilities are only going to grow over time as artificial intelligence continues to evolve and neural networks become larger and more complex. However, human translators’ jobs will hardly ever disappear. There are certain types of content and communication styles that neural machine translation has traditionally struggled with and likely will for the foreseeable future.
“Follow the river and you will get to the sea.”