AI language translation has become increasingly popular, especially as AI is now one of the hottest topics of conversation in the tech world and beyond.

Historically speaking, organizations have heavily relied on human translators to perform high-quality translations for them. Once the world experienced the emergence of internet-based machine translation in the early 2000’s, many organizations (not all) became too dependent on machine translation engines. This oftentimes meant translation quality was sacrificed in the name of time and cost savings.

For years, machine translation has operated in a silo. Until recently.

The Intersection of AI & Language Translation

Enterprises that are increasingly focused on their bottom line seek cost and time savings by way of artificial intelligence, as our society gradually becomes more and more dependent on AI. Language translation is no exception.

The intersection of machine learning and language translation is helping businesses and other organizations across the world access to new audiences, facilitating international growth more efficiently than ever before possible.

Now, you too can put artificial intelligence to work for your business to streamline your translation production, in effect maximizing your time and money. We’re about to tell you how.

How to Use Dynamic Machine Learning to Save Time & Money

In order to reap the benefits of artificial intelligence used in language translation, you must first understand how machine translation software uses machine learning.

You can’t assume that every translation system is using machine learning, so we’ll use our platform, Pairaphrase, as an example.

How Pairaphrase Uses Dynamic Machine Learning

Many machine translation engines are based on frameworks such as Microsoft and Google, but not all of them have been integrated into a platform that allows for machine learning.

For example, as of today’s date you cannot train the Google Translate app to learn your company’s words and phrases to improve your translation output quality over time. In contrast, the Pairaphrase application puts machine learning to use in a dynamic way.

As machine learning requires some form of user input, Pairaphrase requires humans to intervene after translation has been predominantly performed by a machine translation engine. After producing a first draft translation by way of machine translation and Translation Memory (will get to this momentarily), the user trains Pairaphrase to learn its words and phrases by making edits.

Pairaphrase facilitates this by dividing a “first draft” translated file up into segments presented in a Translation Editor tool within the interface.

The user or a bilingual colleague can then make edits to the “first draft” translated file by segment. The system automatically searches for matches of the just-edited segment and will apply that edit dynamically, across the whole file.

The edits are then stored for future automatic use and machine learning in a bilingual repository called a user’s Translation Memories.

As a user makes more edits over time, they are increasing training their machine translation engine. This results in more expansive Translation Memories, and the application increasingly learns the phrases and words the user and its organization uses in their files.

These are auto-populated for future translations in the “first draft” process, during which the machine translation engine pulls existing translation memories into the file where appropriate. As time moves on, there is increasingly less human interference required to produce human-quality translations.

How to Train Your Machine Translation Engine: Step-By-Step

Now that you know the logic behind Dynamic Machine Learning, we’ll show you a step-by-step video to follow so you can learn how to train your machine translation engine using Pairaphrase.

Cheat Sheet for Improving Machine Translation Quality

The cheat sheet in How to Improve Machine Translation Quality teaches you how to improve your machine translation quality by tweaking the way you write your source files.

This will help minimize how much time you need to spend training the machine translation engine in the first place, because the writing style will be better understood by a machine translation engine.

How to Use as Little Human Involvement as Possible in Language Translation

If you have an existing Translation Memory,  you can upload this as soon as you start using Pairaphrase. The application acts as an AI translator to jumpstart your team’s productivity and translation quality. You can also develop a term base glossary and import it.

Pairaphrase also allows you to translate with Amazon Web Services (AWS) translation engine. It enables you to create custom translations on the fly using Amazon’s artificial neural networks and deep learning system (AI translation), which gets you as close to human-quality translation as possible. However, you will need to dedicate a little more time upfront by creating machine translation optimized glossaries.

Experience AI Language Translation First-Hand

Schedule a Pairaphrase demo or share this article with a colleague.

Related: OpenAI Language Translation: Pros & Cons for Enterprises