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A Beginner’s Guide to Machine Translation

Machine translation - What is machine translation?

Looking for ways to cut translation costs and turnaround times without compromising quality? Machine translation might be your answer. Enter the world of machine translation with confidence thanks to our quick guide that walks you through the basics of this growing translation technology.

What is Machine Translation?

Machine translation (MT) is automated translation by computer software. MT can be used to translate entire texts without any human input, or alongside human translators i.e.: machine translation post-editing. MT started gaining traction in the early 50s, and has come a long way since. Currently, the value of the MT market is estimated between USD 130 million to USD 400 million and, as the technology continues to improve, more and more companies are turning to MT to aid human translators and optimize the localization process.

Then And Now: How Has Machine Translation Evolved?

There are several different kinds of MT and as the technology has progressed, the older systems have been replaced by newer technologies.

Rule-based machine translation (RBMT) is the forefather of MT and is now somewhat obsolete. It is based on sets of grammatical and syntactical rules and phraseology of a language. RBMT links the structure of the source segment to the target segment, producing a result based on analysis of the rules of the source and target languages. The rules are developed by linguists and users can add terminology to override the MT and improve the translation quality.

Statistical MT (SMT) started in the age of big data and uses large amounts of existing translated texts and statistical models and algorithms to generate translations. This system relies heavily on available multilingual corpora and an average of two million words are needed to train the engine for a specific domain – which can be time and resource intensive. Statistical is fast being overshadowed by newer technologies but, when using domain-specific data, SMT can still produce good quality translations, especially in the technical, medical, and financial fields.

Neural MT (NMT) is a newer approach that is built on deep neural networks. There are a variety of network architectures used in NMT but typically, the network can be divided into two components: an encoder which reads the input sentence and generates a representation suitable for translation, and a decoder which generates the actual translation. Words and even whole sentences are represented as vectors of real numbers in NMT. Compared to the previous generation of MT, NMT generates outputs which tend to be more fluent and grammatically accurate. Overall, NMT is a major step in MT quality. However, NMT may slightly lack behind previous approaches when it comes to translating rare words and terminology.

Custom vs Generic Machine Translation

Machine translation engines are trained on different kinds of data. Generic MT engines, like Google Translate and Microsoft Translator, are for more general-purposes and are not trained with data for a specific domain or topic. Custom MT engines, on the other hand, are more fine-tuned as they are trained with specific data, resulting in more accurate MT output but also come with a higher price tag. Regardless of whether you are using a custom engine or generic, the engines will need to be retrained from time to time to improve the results.

Thanks to continuous development, improvements have been made to this retaining process. With adaptive MT, the system updates in real-time based on edits made to the content. It is constantly learning and improving.

The Pros and Cons of Machine Translation

So now you have a brief understanding of MT – but what does it mean for your translation workflow? How does it benefit you?

  • MT is incredibly fast.
  • It can translate into multiple languages at once which drastically reduces the amount of manpower needed.
  • Implementing MT into your localization process can do the heavy lifting for translators and free up their valuable time, allowing them to focus on the more intricate aspects of translation.
  • MT technology is developing rapidly and is constantly advancing towards producing higher quality translations and reducing the need for post-editing.

There are many advantages of using MT but we can’t ignore the disadvantages. MT does not always produce perfect translations. Unlike human translators, computers can’t understand context and culture, therefore MT can’t be used to translate anything and everything.

When Should You Use Machine Translation?

In some situations MT alone is suitable, while in others a combination of MT and human translation is best. Sometimes MT is not suitable at all. MT is not a one-size-fits-all translation solution.

For large volumes of content, especially if it has a short turnaround time, MT is very effective. If accuracy is not vital, raw MT (without human post-editing) can produce suitable translations at a fraction of the cost. Customer reviews, news monitoring, internal documents, and product descriptions are all good candidates.

When translating creative or literary content, MT is not a suitable choice. This can also be the case when translating culturally specific-texts. A good rule of thumb is the more complex your content is, the less suitable it is for MT.

That being said, using a combination of MT along with a post-editor opens the doors to a wider variety of suitable content.

Curious about machine translation post-editing? Download our e-book to learn more.

Which MT Engine Should You Use?

There is no specific MT engine for a specific kind of content. Generic MT engines are designed to be able to translate most types of content, however, with custom MT engines the training data can be tailored to a specific domain or content type.

Ultimately, choosing an MT engine can be a long process. You need to choose the kind of content you wish to translate, review security and privacy policies, run tests on text samples, choose post-editors, and several other considerations. The key is to do your research before making a decision. And, if you are using a translation management system (TMS) be sure it is able to support your chosen MT engine.

There is however an alternative to this long process as new tools are being developed to make selecting an MT engine pain-free. Memsource Translate, for example, will automatically select the most suitable MT engine based on language pair, domain, and content type.

Using Machine Translation and a Translation Management System

You can use MT on its own, but to get the maximum benefits we suggest integrating it with a Translation Management System (TMS). With these technologies combined, you will be able to leverage additional tools such as translation memories, term bases, and project management features to help streamline and optimize your localization strategy. You will have greater control over your translations, and be able to analyze the effectiveness of your MT engine. TMSs like Memsource also include specific MT management features like Memsource Translate that selects the best MT engine for your content and Machine Translation Quality Estimation that will streamline your whole MT workflow.

Thinking of implementing MT? Start with a post-editing strategy.

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Author: Lara-Ashleigh Pieterse
Author: Lara-Ashleigh Pieterse

Content Manager at Memsource