Neural Machine Translation: Technology Development that Involves Users Directly

Have you ever translated a sentence with machine translation like Google Translate and the result was out of context now that it was translated word for word? This relates to a system that is used, namely statistical machine translation. However, in 2016, innovation was made involving a new system called neural machine translation. Thanks to its complexity, translation is now more acceptable. Then, how does it work?

How Does Neural Machine Translation Work?

Three years after the neural machine translation innovation was launched, Google Translate perfected it in November and renamed it as Google Neural Machine Translation (GNMT). The system used is not the latest release, but there is an update on how it works. Previously, there was one “brain” for one translation process and now it has turned into one big “brain” that can store data for all languages. For instance, when you translate Indonesian to English, then Japanese to English, the process of translating Indonesian to Japanese will use the information that is already available.

Neural network is a part of a system that runs the way it works. This system can be trained in understanding patterns. The inputted data can be converted into the desired output. On the other hand, there is a mechanism called attention that pays attention to the distinctive visual characteristics of an image. For example, when you input an image of a mountain, the translation results will appear in the form of text.

Another feature of attention is the search for the connection of a translated word so that it can determine the desired sentence structure. In addition, from these connections emerge the strongest value or rating that relates to the hierarchical structure result.

The neural machine translation brain is called the machine translation engine. Just like a blank book, you can fill it with words, sentences, and paragraphs. The more trained, the better the performance because it can distinguish the context of the use of a word.

Machine translation can process words in two ways, namely behavior-based and rule-based or can be called a hybrid engine. Behavior-based functions to understand the situation of word usage, while the rule-based increases the accuracy of translation results through a well-organized grammatical system.

The Advantages of Neural Machine Translation

Neural machine translation is an attempt to automate through previous data collection. Therefore, its use will be advantageous in terms of time spent on a project. In addition, data in the form of languages are more comprehensive, namely 50-100 items or even more. Another advantage that should be considered is that it costs less and can help run workflows with the team through a systematically organized feature from the beginning of work to editing.

Is Neural Machine Translation Perfect?

Neural machine translation is a subset of artificial intelligence. In terms of complexity, it needs to be intensively updated, so its current use still has drawbacks. The thing that needs to be underlined is about the bias of social values. For example, when you translate a job, the subject will tend to one gender even though the context is different.

Referring to its advantages, neural machine translation is still very helpful in working on a project. However, human contribution can improve the quality of the translation through the editing process. Another thing that needs to be considered is the selection of neural machine translation software that suits your needs.

What is The Best Neural Machine Translation Software?

Choosing the best software is neither easy nor difficult, because each of it has advantages and disadvantages. However, there is one thing that can be considered, namely whether there is more than one engine in the software to assess translation performance.

First of all, connect the translation management system with Application Programming Interfaces (APIs). However, there is also software that can connect automatically according to the algorithm. After that, indicators of the effectiveness of a software will be assessed based on the amount of time and expenses.

The current translation management system offers access to other options. It can then assist you to choose a suitable one through a trial. To develop, TMSs also use artificial intelligence to support a feature called Machine Translation Quality Estimation (MTQE). The scores are based on any post-editing process, so it is effective as you do not have to do guesswork to enhance the result.

Although neural machine translation is a very useful tool, the role of humans in translating will never be replaced. The human brain is very complex in understanding a context compared to the performance of a machine. Therefore, neural machine translation does not replace the role of humans, but goes hand in hand because humans can perfect it.

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