How the neural network works and defines the inputted numbers to produce an output is perfected by training the network with millions of sentence pairs. So for example, if you are trying to train a Neural Machine Translation engine for English to Spanish, you would need to feed the engine a great deal of data to help tweak and refine its framework and make it more accurate. Each sentence pair that is given to the engine slightly modifies the neural network while it uses an algorithm called back-propagation. Back-propagation consists of fine-tuning the error rate that comes from the previous iteration. By properly tuning, the error rates can be reduced and the accuracy can be improved.

So what is the advantage of using Neural Machine Translation? Some of the biggest limitations of other machine translation is that they have difficulty when it comes to more complex or nuanced phrases. However, with Neural Machine Translation, it becomes much more possible to translate these kinds of phrases since the number of parameters and rules that can be given provided are much greater and therefore it is more possible to generate translations that are much more natural sounding and closer in meaning.

Neural Machine Translation is without doubt the future of Machine Translation, but there are still reasons as to why it is not widely being adopted. The biggest reason is the sheer cost and time sink that the engines require to actually become useful. As mentioned earlier, millions of sentences need to be entered into the engine for it to start to really output translations that can be considered good quality, and that means using more manpower and time. Not every company is able or willing to invest that many resources to develop a Neural Machine Translation engine, and that is perfectly understandable when weighing the cost versus the reward.

Overall, Neural Machine Translation is a complicated system, but has the most potential out of all of the Machine Translation methods. It is very possible that Neural Machine Translation will be developed to the point where it can accurately be used in a variety of situations for which it is specifically trained, but it is unknown when exactly that might happen. There have been many advancements in its development, but due to its cost and time consumption it will be quite some time before it will be able to be commonly used.

First, there is Statistical Machine Translation, or SMT. The best example of statistical translation is Google Translate. SMTs such as Google Translate use statistical models that draw on a large amount of bilingual text that is provided to them. Through this, they try to find statistical matches for how many times something has been translated as something. For example, if the word “apple” has been translated as “manzana” in Spanish enough times, then that becomes the SMT’s pick for the word apple. It does this for every word in the source and target language before it finally outputs a translation. SMTs can be useful for translating basic words and phrases. However, for more complex sentences, SMTs are not a good pick because they do not factor in context. This means that you can end up with some very strange sentences that do not properly reflect what is being said in the source sentence.

Next, there is the Rule-Based Machine Translation, or RBMT. As the name implies, with RBMT, the machine is translating based on the rules of grammar that it is given. It goes over the source language to analyze its grammar and then looks through the target language to see what kind of grammar rules need to be followed when translating the sentence. After it is done, it translates the sentence according to the rules that it looked up. While this might sound as if there might be fewer problems than SMTs, RBMT translations still need a great deal of proofreading to ensure that there are no problems with the text.

Third, there is the Hybrid Machine Translation, or HMT. The HMT takes elements of RBMTs and SMTs and then uses a translation memory. The addition of a translation memory makes the overall quality of the translation higher than that of SMTs and RBMTs, but there are still problems with using a HMT. For one, it still needs extensive editing, meaning that human translators will be needed to review all the translations.

The fourth, and final type of translation is Neural Machine Translation, or NMT. This type of machine translation uses neural network models that are based on the human brain to make statistical models for translation. When they work, NMTs can provide some of the most accurate translations that would need little to no editing potentially. One of NMT examples is DeepL.

Overall machine translation can be used as a tool to expedite translation, but whether it can produce clear and accurate translation for high level discussions, complex concepts, nuances and context that is still in question and requires time to advance it. This once again proves that the value of professional human translators is not replaceable just yet!