Google Translate vs DeepL. 5 applications of machine translation for business | AI in business #8

What is neural machine translation?

Neural machine translation is an advanced form of translation that employs deep neural networks to analyze and translate text. Unlike previous methods that translated text according to set rules or statistics, neural machine translation analyzes entire sentences, taking into account context. It is used by state-of-the-art tools such as Google Translate, DeepL, Microsoft Translator and Yandex.

Advantages and disadvantages of machine translation

The main advantage of machine translation is its ability to create smoother and more natural translations. For example, while earlier versions of Google Translate translated each word individually, modern neural technologies can correctly interpret the word in context. The disadvantage, however, is the need for access to huge databases and advanced computing power.

Google Translate. The most popular choice

Google Translate, launched in 2006, initially relied on statistical methods. However, thanks to investments in neural technology, the quality of translations has improved significantly. Supporting more than 130 languages, Google Translate can translate:

• texts – when pasted into the translator window in the web browser,
• images – especially useful for translating languages that use a different notation than the language into which you are translating; supports .jpg, .jpeg, and .png formats,
• text files – in .docx, .pdf, .pptx, and .xlsx formats
• entire web pages – after pasting the page address, the translated text remains in its original place.

It is also integrated with many Google services, such as the Chrome browser and Google Docs, making it easily accessible to users around the world. You can also use the API, meaning you can use Google Translate to automatically translate your site or application.

The translator from Google is available on the web, Android and iOS platforms. One of Google Translate’s most practical features is translating web pages via URL, something its competitor, DeepL, does not have.

DeepL. Rising star of machine translation

DeepL has quickly gained recognition for its ability to deliver higher quality translations than its competitors. DeepL trains its neural networks with the Linguee database, which allows for more accurate translations. It currently supports 28 languages and offers unique features such as a translation dictionary and tone customization.

What’s more, DeepL offers a paid Pro version that provides additional features such as a larger character limit and API access. It is available on the web, desktop (Mac and Windows), Android and iOS platforms.

Google Translate vs DeepL. Comparison

Although both platforms adopt neural technology, they differ in several key aspects.

• Translation accuracy – DeepL typically scores better than Google Translate in blind tests, especially for European language pairs. In tests where translations were evaluated, DeepL often had better translation results. In addition, DeepL translations are more natural, especially for European languages.
• Supported languages – Google Translate supports more than 130 languages, making it the winner in this category. In contrast, DeepL supports only more than 30 languages. Although both services cover popular languages, Google Translate offers more options for less popular languages.
• Integrations/Options – Both services offer web interfaces for casual translation. DeepL offers a desktop app for Windows and macOS, while Google Translate does not. Both have mobile apps. For web translation, both offer API services.
• Price – Both Google Translate and DeepL offer free web versions. When using the API, both have a free limit of up to 500,000 characters per month. Google Translate charges $20 per million characters after the free limit is exceeded, while DeepL has a fixed rate of $5.49 per month plus $25 per million characters.

5 uses of automatic translator for business

Modern businesses are increasingly exploiting automatic translations. Thanks to them, it is possible to quickly and efficiently translate documents, websites or communications with customers from different countries.

1. Automatic translation of documents

In the business world, where time is money, speed of translation is key. Imagine a multinational corporation that receives hundreds of documents in different languages every day. Instead of waiting days or weeks for a translator, you can take advantage of DeepL or Google Translate for quick translation and preliminary analysis.

2. Localization of websites and applications

Today, a multilingual online presence is key to global success. With automatic machine translation, companies can easily and quickly localize their websites and applications for different markets.

3. Real-time voice translation

This technology has great potential, especially in the travel and hospitality sector. Imagine a hotel that uses real-time voice translation to communicate with guests from different countries. This not only improves the customer experience but also opens the door wide for international customers.

4. Automatic translation of the text on image and video

In the age of social media, video content is king. With automatic subtitle translation, companies can easily tailor their video content to different markets. This not only increases reach but also engages international audiences.

Summary. The present and future of automatic translation

Automatic translation has become an integral part of business in the era of globalization. The choice between Google Translate and DeepL depends on a company’s specific needs. One thing is certain: Machine translation technology will continue to evolve, offering better and better solutions for business.

In the future, we can expect machine translation to operate in increasingly sophisticated applications, such as real-time translation during video conferencing or even automatic translation of thoughts directly into the language in which we want to communicate, using brain-computer interfaces.

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