Languages Supported by Popular Machine Translation Engines

Over the years several machine translators have been developed and so, we took it upon ourselves to research these engines. In this article, we hope this detailed resource serves as a valuable guide for your language service needs.

We will examine the most popular machine translation engines, how they work, and their language support. We will also discuss how to evaluate these engines and provide tips for using them effectively. The engines covered here include Google, DeepL, Amazon, ModernMT, Microsoft, ChatGPT, LibreTranslate, IBM, Lingvanex, Niutrans, Royalflush, and Groq.

List of Machine Translation engines:

Amazon

ChatGPT

DeepL

Google Translate

Microsoft Translator

ModernMT

Systran

Yandex

Popular Machine Translation Engines and The List Languages They Support

**data last updated June 2023

Criteria for Selecting the Best Machine Translation Engine

Discover the six key criteria we've developed over the years to help you choose the best machine translation engine. Learn what to look for and make informed decisions for your translation needs:

• Translation Accuracy

• Language Support

• Contextual Understanding

• Speed and Efficiency

• Customization and Adaptability

• Cost and Accessibility

1. Translation Accuracy

Translation accuracy is paramount when selecting a machine translation engine. Evaluate the engine's ability to produce grammatically correct and contextually appropriate translations. This involves testing the engine with various language pairs and content types to assess its reliability.

Read more: Unlocking Excellence Through Automated Machine Translation Quality Evaluation

2. Language Support

A machine translation engine should offer comprehensive language support, particularly for the languages most relevant to your needs. Extensive language coverage ensures the flexibility to handle a wide range of translation requirements efficiently and effectively.

3. Contextual Understanding

The ability of a machine translation engine to understand and translate context accurately is crucial. This includes recognizing idiomatic expressions, cultural nuances, and industry-specific terminology. Engines with advanced neural networks tend to perform better in contextual understanding.

4. Speed and Efficiency

Evaluate the speed and efficiency of a machine translator, especially for high-volume projects. An efficient engine should provide quick turnaround times without compromising translation quality, enhancing overall productivity.

5. Customization and Adaptability

Customization features such as glossaries, translation memories, and adaptive learning capabilities can significantly improve translation accuracy and consistency. An engine that allows for customization can be tailored to meet specific industry requirements and terminology.

6. Cost and Accessibility

Consider the cost-effectiveness of the machine translator, including subscription fees, pay-per-use charges, and any additional costs for premium features. Accessibility in terms of ease of use, platform integration, and customer support is also an important factor.

Read more: 5 Considerations When Measuring Machine Translation ROI

Machine Translation Engines for In-Demand Languages

In selecting the machine translators below, we evaluated their performance in these languages: French, German, Spanish, Chinese, Japanese, and Korean. We chose these languages because they are widely used globally. Here is how the different machine translators perform with these languages:

Google Translate

Strengths: Google Translate is good for French, German, and Spanish. It provides quick and reasonably accurate translations and supports voice and image translation.

Example: Google Translate can quickly translate restaurant menus, providing a convenient and fairly accurate understanding of the available dishes.

Microsoft Translator

Strengths: Microsoft Translator is excellent for Asian languages like Chinese, Japanese, and Korean. It integrates well with Microsoft Office, providing real-time translations.

Example: Translating a technical manual from English to Chinese with Microsoft Translator ensures accurate use of technical terms.

DeepL

Strengths: DeepL excels with European languages, particularly German, French, and Spanish. It produces very accurate translations by understanding the nuances of these languages.

Example: Translating a German legal document with DeepL ensures precision and correct use of legal terms.

Best Practices for Using Machine Translation Engines

In researching for this article, we interviewed some of our project managers. Here are their tips and advice for using these engines efficiently and effectively:

1. Clear Input Text

For the best results, start with a clear and error-free text. Proofread the text to remove any spelling, grammar, or punctuation mistakes. Avoid using slang, idioms, or ambiguous language. Use simple and straightforward sentences to improve translation accuracy. Machine translation engines work best with clear and concise input.

Read more: Effective Strategies for Minimizing Machine Translation Errors

2. Post-Editing

After the machine translation engine produces a translation, it is important to review and edit the text. Check for any errors, inconsistencies, or awkward phrasing. Post-editing allows you to fix mistakes and adjust the translation to fit the context and intended meaning. This step is crucial for professional or official documents, where precision and clarity are essential.

Read more: The Art of Translation: A Deep Dive into Light vs. Full Post-Editing

3. Customization Options

Many machine translation engines offer customization features to improve translation quality. Use tools like glossaries and translation memories to increase consistency and productivity. Glossaries let you set preferred translations for specific terms. Translation memories store previously translated segments for future use. These options help maintain uniformity and ensure accurate translations of specialized vocabulary.

Read more: Mastering Translation Efficiency: A Guide to the Top CAT Tools in 2023

4. Understanding Limitations

Machine translation engines have improved, but they have limitations. They may struggle with complex or specialized texts that need deep understanding. Cultural nuances, idiomatic expressions, and technical language can be hard for them to translate accurately. For these texts, having a professional human translator is often needed for the best accuracy and appropriateness. Knowing these limitations helps you decide when to use machine translation and when to seek human expertise.

Choosing the Right Machine Translation Engine

Choose a machine translation engine based on your specific needs, such as the language pair, industry, and project requirements. Google Translate is good for general use, while DeepL is ideal for high-accuracy needs in European languages. Understanding each engine's strengths helps you make the best choice.

Conclusion

Machine translators are vital for bridging language gaps and enabling global communication. They support a wide range of languages, helping people understand each other and share information. Choosing the right machine translation engine and using it effectively ensures high-quality translations.

Looking to explore the above-mentioned engines and use their features? You can try all these machine translators on our homepage for free or by signing up for our free subscription plan, which offers 1,500 free credits per month. Our AI-powered machine translation aggregator evaluates, compares, and analyzes 12 different machine translation engines to provide the most accurate results.