The Big Guide to Machine Translation

Image: a graphic showing a translation process with machine translation

Simply put, machine translation (MT) is a process where a computer program automatically translates text from one source language to a different target language. Machine language translation has a long and interesting history dating back to the 1950s.

Over time, the technology has developed into a viable solution for fast and accurate translations. Advances in artificial intelligence (AI), natural language processing (NLP), and computing capabilities brought machine translation into the mainstream.

Benefits of Machine Translation (MT)

Machine translation is an indispensable tool in the translation process. It can be used alone or in combination with human post-editing. MT offers three primary benefits for your translation workflows:

Fast Translation Speed

Machine translation can translate millions of words for high-volume translation projects. But speed isn’t the only benefit! MT uses AI to get smarter as more content is translated. Plus, MT can work with a TMS to manage and tag high-volume content. This helps you stay organized when you need to quickly translate content into multiple languages.

Excellent Language Selection

Most major machine language translation providers can translate 50-100 languages. These programs are powerful enough to translate multiple languages at once so you can roll out global products and documentation updates. MT is well-suited to language pairs such as English to French or English to Spanish.

Reduced Costs

Even when human translators are needed for post-editing, MT cuts translation delivery times and costs. MT takes care of the initial heavy lifting by producing basic but useful translations, which a human translator can refine and edit. This way, the finished versions will adhere more closely to the text’s original intent, and the content can be effectively localized.

Types of Machine Translation

There are four different types of machine translation–Statistical Machine Translation (SMT), Rule-based Machine Translation (RBMT), Hybrid Machine Translation (HMT), and Neural Machine Translation (NMT). Here’s an overview of each type:

Rule-Based Machine Translation (RBMT)

RBMT— the earliest form of MT— translates content based on grammatical rules. There have been significant advances in machine translation technology since RBMT was developed, so it has a few disadvantages. These drawbacks include the need for large amounts of human post-editing and adding languages manually. Despite this low translation quality, RBMT is useful in basic situations where a quick understanding of meaning is all that is required.

Statistical Machine Translation (SMT)

SMT works by building a statistical model of the relationships between text words, phrases, and sentences. It then applies this translation model to a second language and converts the same elements to the new language. SMT improves somewhat on RBMT but still shares many of the same problems.

Hybrid Machine Translation (HMT)

HMT is a blend of RBMT and SMT. HMT leverages a translation memory, making it far more effective in terms of quality. However, even HMT has its share of drawbacks, the greatest of which is the need for human editing.

Neural Machine Translation (NMT)

NMT employs artificial intelligence to learn languages and improve that knowledge constantly. In this way, it strives to mimic the neural networks in the human brain. NMT is more accurate than other types of AI translation. With NMT, it’s easier to add languages and translate content. Because NMT provides better translations, it is rapidly becoming the standard in MT tool development.

NMT works by incorporating training data. Depending on the user’s needs, the data can be generic or custom.

    • Generic Data: This is the total of all the data learned from translations performed over time by the machine translation engine (MTE). This data produces a generalized translation tool for various applications, including text, voice, and documents.
    • Custom or Specialized Data: This is training data fed to a machine translation engine to build specialization in a subject matter. Subjects include engineering, design, programming, or any discipline with its own specialized glossaries and dictionaries.

Considerations for Machine Translation

Here are some factors you should consider when choosing an MT tool for your project:

  • Budget:
    Neural machine translation is sometimes more expensive to train than SMT, but the translation quality improvement can justify the expense.
  • Industry:
    Some industries involve translating complex and technical language, which requires the more sophisticated processing that NMT provides.
  • Language Pairs:
    SMT works best for certain language pairs. For example, Latin-based languages with similar syntax and linguistic rules are the most compatible with machine translation.
  • Amount of Content:
    NMT requires large quantities of source text to process and learn from, so it’s not a good fit for small projects.
  • Customer-Facing vs. Internal Content:
    Customer-facing content, such as sales or marketing materials that reflect brand quality, needs the most sophisticated combination of machine translation and human post-editing with qualified translators. When cost and time are factors, basic internal documentation or employee communications can be translated with basic MT.

Which Machine Translation Engine Is Best?

Prominent tech players like Google, Amazon, and Microsoft use NMT to power their machine translation engines (MTEs). When we compare different engines, it is essential to understand that they are constantly learning and improving. Read on to learn about top machine translation engines.

  • Google Translate

    Logo for Google Translate (Google's Machine Translation software)
    Google Translate was the first MT engine to use neural language processing and employ machine learning from repeated use. It’s generally considered one of the leading machine translation engines based on usage, number of languages, and integration with searches.

  • Amazon Translate

    Logo for Amazon Translate (Amazon's machine translation tool)
    Amazon Translate is closely integrated with Amazon Web Services (AWS). Some evidence suggests Amazon Translate provides more accurate translations of certain languages, notably Chinese.

  • Microsoft Translator

    Logo for Microsoft Translate (Microsoft's Machine Translation AI)
    Microsoft Translator integrates with products like MS Office and Skype. This feature provides instant access to translation in documents and compatible programs.

  • Watson Language Translator
    Logo for IBM Watson: IBM's Machine Translation Tool

    The Watson Language Translator is the MT tool from IBM. It integrates with IBM Watson Data and IBM Watson Studio. These tools help manage data and build AI models.

  • DeepL Translate

    Logo for Deepl machine translation services
    DeepL Translate is an independent MT engine produced by a small company in Germany. Thanks to the company’s proprietary neural AI, DeepL provides natural-sounding and nuanced translations. Worldwide use of Deepl has vastly increased in recent years.

Choose a Translation Management System (TMS) with Built-In Machine Translation

Integrating MT into your translation and localization strategy is a must. Localize speeds up your workflow with a built-in machine translation service. Our platform then provides easy access for your professional translators to post-edit your machine translations. The result is a high-quality translation.

Unlike many translation services, Localize does not charge extra for machine translation. We offer free integrations with Google, Amazon, Microsoft, Watson, and DeepL.

Contact our experts at Localize today to explore how our solutions can help you.

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