Often people mix machine translation (MT) with neural machine translation (NMT). Originally, neural machine translation was an adaptation of machine translation. It is a more revolutionized version that provides faster results with a better accuracy rate. You can also customize your neural machine translation tools according to the requirements of specific domains. 

One benefit of neural machine translation to mention here is that it needs less human input. Putting these benefits aside, let’s focus on the ethical consideration in NMT which is the main target of this read. We also discuss how MT differs from NMT.

What is Neural Machine Translation?

Neural machine translation is a state-of-the-art approach that provides organizations with accurate translations with the use of NMT engines. It is a software resulting from technological advancements in machine translation tools. This system works on the input that translators provide. The accuracy of that input results in a well-translated text.

The insertion of neural networks in NMT makes it flexible to translate a huge amount of data in no time. For instance, NMT is often used for technical translation services where large volumes of data must be translated. 

How Does Neural Machine Translation differ from Machine Translation?

MT uses rule-based systems for translation solutions. On the other hand, NMT utilizes recurrent neural networks (RNNs) to form an input model for the conversion of source language into target languages. It is a drawback of machine translation that it often loses grasp over the background information or the context of translation. But with neural machine translations, this problem never arises as this system is more adept.

The adjustment of parameters is different in both systems. It is quite easy to adjust the weights in the neural network. Simply speaking, these adjustments are to give the system better input to enhance the quality of translations. With machine translation, the adjustment of parameters involves the change in linguistic rules.

The rate of interpretability is greater in machine translation than in NMT. With the involvement of rule-based systems in machine translation, linguists easily retrace the translation solutions via the pattern. While with NMT, the involvement of neural networks makes it difficult to interpret. It means that you can’t track the working of an NMT model because of its complexity.

Moreover, the automatic translation of source text with the help of NMT engines is a cost and time-effective method that has a specific algorithm to process the input data and then translate it into the target languages. The skill of these translation services goes beyond word-to-word translation. Despite the accuracy of translations, human translators do detect some of the flaws in translation and proofread it for further mistakes. It means, that despite being an efficient system, it does have its flaws. This also implies that MT might be suitable for electronics translation services where simple translation is sufficient. But for marketing content where a creative approach is required, human input is quite important. 

Ethical Considerations in NMT

Before rooting for NMT for your translation needs, it is important to look out whether this system is best for you or not. 

Find a Sweet Spot Between Domesticating & Foreignizing

Don’t you think that making use of MT for technical translation services to tackle a long text is a smart move? It sure is! However, when it comes to neural translation, it is the job of the translator, giving input to the neural tools, to create a balance between domesticating and foreignizing. 

Do you know what these terms are? Let me just explain to you.

Domesticating is the adjustment of the translation solutions according to the context, tone, and cultural nuances of the target language. And why is it done? To reduce any strangeness or weirdness of the target text for the new audiences. 

Foreignizing, on the other hand,  is targeted at retaining the tone and context of the source language in translation. It means that the original tone with which a concept is described in foreign languages is preserved in its modified version. 

When you find that balance between these concepts, maintaining the quality of translation becomes way simpler. But it’s easier said than done. 

The Responsibilities of the Agent

But who or what this agent is? The person held responsible for checking the ethical nature of the translation. 

If the translator is using an NMT tool, they are the agent in this case. It is their job to look for the post-editing of machine-generated results. 

What if the client is dealing with NMT tools? Well, in that case, the neural machine translation will be the agent. So you need to know who is responsible for translation. If you don’t know them, you’ll be unable to fix the errors which may jeopardize the entire process. 

In the case of NMT, you do need to alter the input and set its tone according to the target language.

Let’s Close our Discussion

In simple and plain words, NMT is the most advanced machine translation technology that works on the process of giving input to it. It makes use of an error-and-trial process to train itself, just like the way a human brain does.

The result of this input? It’s the desired output your client gets which sounds quite human. Isn’t it interesting? To achieve that level, it is important, however, to create an equilibrium between domesticating and foreignizing. Only then, you can expect the NMT to meet the preferences of your audience.