Free Machine Translation Engines: Top of the Class
AI and machine learning have advanced dramatically in the last ten years. We are witnessing them permeate all sectors and industries, including translation. Machine translation engines are not necessarily new. But as they improve beyond the realms of tourists asking for directions, so make the potential applications.
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Optimizing Translation Efficiency with Machine Translation Software
Of course, machine translation (MT) software is not better than a human translator, but it can still be helpful in many situations.
For example, it can be used to automatically translate text that is posted on your website.
You could hire a human translator to translate your website, but this can be expensive, especially if you need to regularly keep the website updated with new content. A more cost-effective solution is to use machine translation and interweave it within a translator’s workflow.
Another good use case for machine translation can be the translation of emails or support tickets. It can be beneficial when communicating with international customers or suppliers.
In addition, machine translation software can be used for document translations, especially if you need the translation ASAP.
Of course, the quality of MT depends on several factors, such as the quality of the source text, the subject matter, the complexity of the text, and the languages involved. In general, the quality of MT is better for shorter texts and simple phrases.
If you want to learn more about machine translation science, check out our blog series dedicated to neural networks and transformers which power up machine translation engines.
Key Features for Effective Machine Translation Software Selection
Some of the features that you should look for in machine translation software include the following:
- Ability to translate multiple languages and customize the translation
- Ability to use your preferred terminology and style
- Ability to integrate with your existing translation workflows and content management system (CMS)
- Ability to translate multiple file types and extract content from these files in such a way that the MT engine has an easy job with translating ‘clean’ sentences.
Unfortunately, all publicly available free machine translation systems only offer some of the mentioned features. Additionally, they limit the size of the content that can be translated.
Nevertheless, these systems offer translation services instantaneously, and, of course, the tradeoff is accuracy and loss of nuance. Let us unpack the top choices below and see how they stack up.
#1. Google Translate
You know the name, and you most probably used it yourself at one time or another. Google Translate is the most widely used (at a consumer level) machine translation engine – in no small part due to its integration into Google’s monolithic search engine.
With more languages and language combinations available than the competition, it’s easy to see why Google Translate is often the first choice when it comes to machine translation engines.
Google Translate began life as a Statistical Translation System or in layman’s terms, a word-by-word translation system. Of course, when translating in this manner, little sense is often made from the final result, and invariably the results lacked crucial context.
However, back in 2016, Google altered its machine translation engine to a neural machine translation system. Utilizing deep learning techniques, it now translates entire sentences at a time. Furthermore, it grants a higher degree of accuracy and understanding while also affording a small measure of context. But given its existing advances and continued evolution, as of 2018, it translates more than 100 billion words a day.
Microsoft Translator followed a similar route to that of Google Translate. It started as a statistical machine translation engine before adopting a neural machine translation approach.
Again its popularity likely stems from its integration into other Microsoft services, the likes of Bing, Skype, Microsoft Office to name but a few. As of May 2020, the service supports 73 different language systems. It is also making major strides in speech translation systems with 11 languages available.
Like most modern machine translation engines, Microsoft Translator is data-driven and relies on algorithms to interpret and translate. With so many similarities to other translation engines in the means of translation, it will be the application of said technology that sets them apart. In Microsoft’s case, the impressive Conversations which allows on the fly translation in a chatroom environment. Additionally, each user has the added capability to select their language before entering.
Microsoft Translator’s API for business applications requires a subscription plan. To get started, you’ll need to create a Microsoft Azure account and get a Client ID and Client Secret. This information is used to authenticate with the Microsoft Translator API. Once done, you’ll be able to create your first project in your preferred IDE and deploy Microsoft Translator in your app.
DeepL is another consumer-facing, free neural machine translation engine. What it lacks in the scope and scale of Google and Microsoft, it more than makes up for inaccuracy. The service uses Convolutional Neural Networks, which are proven to work exceptionally well in the field of Natural language processing (NLP).
Currently, DeepL offers translation between 13 languages and 110 language pairs. The developers are confident that their unique AI will consistently outperform the competition in terms of the accuracy of the translation. Also, providing a more natural translation, complete with more context and nuances. FYI, DeepL is operated by a hydro-powered supercomputer in Iceland!
#4. Amazon translate
While Amazon Translate may not be as established as other MT solutions, it’s certainly one to keep an eye on. As one of the largest online retailers in the world, Amazon has a significant impact on e-commerce translation trends. Unsurprisingly, the company has invested in developing a translation service of its own. Amazon constantly enhances its datasets to extend the number of use cases. Its Active Custom Translation feature allows users to import their own translation data for customizing translations according to their preferences.
You need to set up the translation environment to start using Amazon Translator. This is a two-part process. First, you must create an Amazon account and set up AWS credentials. You will need to create an IAM user. Afterward, you will need to generate access keys for this user and store them in the environment variables.
Leverage machine translation software for your localization projects today.
Keep in mind that more than generic MT software may be required for the needs of your business, as it is not meant to replace a human translator.
You should have a customized machine translation engine that works best with your content. Moreover, having a professional translator in the loop is crucial to fine-tune the translation and ensure it is fit for purpose.
On top of it, you should have a system that will pre-condition your content, make it optimal as the input for a machine translation engine. Additionally, it should prioritize human over machine translation during automated translations.
TextUnited is the perfect solution for providing these additional layers of translation automation. It caters for many different content types – from the translation of documents to websites, online catalogues, and chat messages.
It is also built for team collaboration among your teammates and professional translators. This allows you to get expert eyes contributing to your translation before publishing.