Breaking Down an AI-for-translation


As artificial intelligence (AI) grows, so do its potential applications. One such application is machine translation, or the ability of a computer to translate text from one language to another. 

In this article, we’ll look at how AI is being used in machine translation and some of the challenges that remain.

What Is AI-For-Translation and How Does It Work?

Machine translation has been around us for a long time, but early approaches involved translating individual words and sentences one at a time. More advanced statistical models allow for whole sentences to be solved by considering the probabilities of all possible translations. For example, we might model the likelihood that the English sentence “I am hungry” is followed by the French sentence “Je Sui’s farm”.

Statistical models are great because they can learn from large amounts of data. However, they have a significant shortcoming: the quality is only as good as the training data used to build them.

For example, if most of your training data translate between French and English, your model poorly translates to Latin. Likewise, if your training data only contains short sentences, your model produces poor translations for long ones.

How to Get Started with AI-For-translation?

Machine translation is a complex problem. Its complexity comes from the fact that it has to work across languages, where there can be semantic and cultural differences and large numbers of exceptions. Fortunately, the field of machine learning excels at tackling complex problems. With enough data and computational resources, we can solve even tricky situations.

While building machine translation systems is still tricky, great strides have been made with open-source tools. For example, Google’s Neural Machine Translation (GNMT) design won the WMT machine translation contest for six of 10 languages.

One common way to use machine learning is through feature engineering, which means taking raw data and transforming it to match the input or output of machine learning models. Of course, each model may have different features, so no single feature set works for every model. However, experience is an excellent source for creating new features.

Considering all possible translations can be overwhelming even with large amounts of data and sound feature engineering. Computers are fantastic at doing the same thing repeatedly, so one way to simplify this is for humans to label translations. This technique not only speeds up translation but also helps improve it.

How can AI-For-translation Help Your Business?

Today, most machine translation is done using linguistic rules and supervised learning. However, the quality of such models varies across industries and use cases. Machine learning (ML) models that can learn from less supervision have been out-performing these rule-based approaches for some years now, particularly in the domain of text translation. 

It means that businesses that rely on machine translation, such as international financial institutions or online travel companies, can benefit from leveraging AI technology. Applying machine learning to the world of text translation means that these businesses no longer have to spend vast amounts of money on pre-translated content or hire teams of translators. 

Instead, they can use algorithms that learn directly from their data and require no prior knowledge of the language pairs for training.

How Can We Improve Machine Translation?

There are a few methods that computer scientists use to create more robust models:

  • Collect and Label Large Amounts of High-Quality Data

Translating written text is a difficult, time-consuming work. It’s often easier for humans to create the data than writing code that performs this task.

  • Design Algorithms That Can Learn from Small Amounts of High-Quality Data

Getting more data means spending more money. Many organizations are exploring creating smaller training datasets while still achieving accurate results.

  • Design Algorithms That Require Less Supervision

One of the most challenging parts of building a machine learning model is figuring out how your data is structured and what features are relevant for solving your problem. 

If you have to give the computer precise instructions on what to look for, it can end up taking hours or days to train your model. However, if you can design algorithms that work without this level of supervision and allow the computer to learn by itself, it can take minutes or hours instead.


As your organization grows, you’ll want to provide more services for your customers worldwide. Adding new languages is possible with machine translation, but rule-based systems often fall short of desired accuracy and speed requirements. We also recommend using a high-speed internet connection; check out Spectrum deals for customized packages. Find bundles for your internet needs at the right prices from Spectrum Customer Services

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