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What Is Neural Machine Translation?

The goal of artificial intelligence is to create a machine that is able to understand and communicate like a person. A crucial part of reaching that goal involves a machine that is able to understand and interpret language. This process has led to the development of neural machine translation technology.

Find out what neural machine translation is and how it is helping create an AI-driven digital transformation.

How Does Neural Machine Translation Work and What Is It?

To understand neural machine translation (NMT), it’s important to understand machine translation. Machine translation is the process by which a machine, or rather a software program, translates text from the native language to a target language.

Neural machine translation tries to achieve that goal of machine translation by using neural network models.

What Is a Neural Network?

A neural network is a complex program that reflects the behavior of human processing and pattern recognition. At the most basic level, a neural network starts with no programmed understanding but then gains experience through failure, learning what does and doesn’t work to perform any given task.

Neural networks solve problems by leveraging machine learning and deep learning. Neural networks are complex tools that can be used in everything from an AI learning how to play Mario to machine translation.

How Is NMT Different from Rules-Based Machine Translation (RBMT)?

Rules-based machine translation (RBMT) is the most basic form of machine translation. A strict set of rules is given for translating from the native language to the target language, and there is no room for growth.

Trying to compare RBMT with NMT is like trying to compare the original Apple-1 to a modern computer. While there are similarities between them, the more modern NMT is significantly more accurate than the RBMT.

Confined by Rules

The easiest way to think about RBMT is to think of an English-to-German dictionary. The dictionary provides a one-to-one translation for each word or phrase, and the machine follows those rules. The problem with RBMT is that countless ideas and phrases have more than one equivalent, depending on things like the level of formality, or the emotional resonance.

Similarly, countless structures in one language do not have a one-to-one translation to other languages. Think about idioms like “off the top of my head” or “the last straw.” Directly translating an idiom is an easy way to make incomprehensible gibberish because you aren’t translating the meaning or idea behind the idiom.

While other languages have their nuances, these hidden layers of meaning are part of the reason English can be one of the more difficult languages to learn and translate fluently. The basic rules of English don’t take long to cover, but the exceptions to those rules, the difference between formal and informal English, and the variations between all of those exceptions can take a lifetime to master.

Limited by Programming

There is an added problem if a word is not in the programmed resources-it becomes impossible to translate. This is one of the many reasons RBMT is no longer widely used for machine translation. 

RBMT can be a decent starting point for translation, but language is more complex than simple one-to-one substitution. The nuances and meanings behind words are lost, and when used for much more than a starting point, it can lead to very inaccurate translations.

How Is NMT Different from Statistical Machine Translation (SMT)?

In neural machine translation, the software uses machine learning to continually develop and grow. This means that as languages naturally develop over time, or the software is introduced to specific industries that use unique vocabularies (like the medical or legal field), the neural network is able to develop new processes that enable it to provide an accurate translation. The more exposure the neural network has to the task, the more accurate it becomes.

Statistical machine translation (SMT) uses neural networks but develops in a different format. SMT learns how to translate by focusing on existing successful translations between the original and target languages. SMT focuses on the existing data it has access to and uses those examples to determine the correct way to translate text.

The Limitations of SMT

Statistical machine translation can be an accurate way of learning the basics of translation, but it has limits. For example, if SMT is shown nothing but translations of medical texts between English and Spanish, it will develop a strong neural network that is accurate, but only in that one field.

So if it is tasked with translating something that is not medical, like a local news report about a state champion high school football team, it doesn’t have the necessary neural network to provide an accurate translation.

While both SMT and NMT use neural networks, the biggest difference is in how those networks are developed. For SMT, once the neural network develops through exposure to successful translations, the network is set, and there is no further development to come. On the other hand, NMT allows for continual improvement to the neural network.

How Does Neural Machine Translation Software Identify Mistakes?

Neural networks make progress and learn, but a network can’t identify mistakes it makes without manual help. A human translator works with the neural network to help identify and correct any mistakes, errors, or struggles with translation. This is an integral part of using a neural network because it allows for the network to continually grow and improve its algorithm.

Will Neural Machine Translation Ever Replace Human Translators?

There’s no need to worry-the machines are not taking over, and there’s no need to panic. Neural machine translation will never be able to perfectly replicate human translators. While NMT is becoming more powerful and accurate, language is continually growing and shifting the rules that it used to follow.

Consider the pronoun they. Although it’s been used to refer to individuals for hundreds of years, formal writing and speech generally only allowed he/she as singular pronouns. But language evolves, and official sources have caught up as common usage has only become more prominent. Recently, they has even been recognized by the dictionary to officially allow for singular usage, both for nonbinary individuals and in cases where someone’s pronouns are unknown.

NMT can be very accurate once it learns the rules and expectations of a language, but it does not have the ability to keep up with the natural and continual growth and development of languages. It has to rely on a human translator to fill in the gaps.

When Should I Use Neural Machine Translation?

If you use any sort of machine translation, there’s a good chance you’re already using NMT. NMT is the best machine translation option on the market right now, so you should use NMT for all of your translation needs. Most translation programs you can purchase and even some free online providers use NMT. But remember that NMT requires human oversight to grow and develop the way it’s designed to.

Automate Translation Using NMT

With the ever-growing digital age, our world is becoming more connected than ever. Being able to accurately translate content quickly and efficiently isn’t optional; it’s a necessary part of communication and business. NMT makes translation faster, more accurate, and easier to use for everyone. Learn more about NMT or business intelligence software with SYSTRAN.

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