The first one is how to search for an exact phrase. While a dictionary can provide the exact word you want, there are often times where the word might not be used the way you think it should be, or there might be specific contexts in which it can be used or it might just be an outdated term altogether. To find out if a word or phrase you want to use can be used the way you want it to be, you can perform a Google search and put the term or phrase in quotation marks. So, for example, if you wanted to search for only the phrase raining cats and dogs, you could put it in quotation marks, “raining cats and dogs,” and then every single result will be websites that have the words in that exact order. You can also use the “verbatim” search function to search for only the word that you enter.

Next, if you want to find alternatives to words in phrases, you can use Google to search for alternatives. For example, if you wanted to search for different types of machine translation in one search, you could search like this: neural OR statistical OR rule-based machine translation. When you do that, Google will generate search results for machine translation that includes the words neural, statistical, and rule-based. This is handy if what you are searching for has numerous variations to it, but you only need a few alternatives as opposed to all of them.

On the flip side, there is a way to exclude search results from appearing. So in the case that you want all the different variations of a phrase but not something else, then you can use the minus sign to exclude the results that you don’t want. So if you want all the different kinds of machine translation, but don’t want statistical machine translation, then you can search –statistical machine translation and it will exclude all mentions of statistical machine translation. This is great if you know that you don’t want a search result to appear and are looking for other things instead.

One of the simpler tricks that does not require any special input is just comparing the amount of results that one version of a phrase generates versus another. So if you were wondering which spelling of versus is more commonly used, you could search for “vs” first and see how many hits are generated and then search “versus” and then see how many hits are generated. After comparing which one generates more, you can more confidently say that the one with more hits is the more commonly used word and therefore, safer to use in your translation.

The last trick is searching within a specific website for a specific phrase. If you wanted to look for the phrase “machine translation” on SDL’s website for example, you could search it as follows, site:sdl.com “machine translation” and then Google will generate all the instances of machine translation being mentioned on SDL’s website.

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!