First of all, I
am going to start by explaining what the machine translation is. Machine
translation is “translation carried out by a computer”, as defined in the
Oxford English dictionary. Even though it looks simple, It is not as simple as
it looks. You can think as the machine is going to do all the job for you but
at first, you have to know how the machine actually works. But, how does the
machine translation actually works; There are three basic systems of machine
translation: Rule-based system, Statistical system and Neural Machine
Translation system. Rule-based systems use a combination of language, grammar
rules and dictionaries for commonly used words. Statistical systems do not rely
on language rules but they learn how to translate for specific industries and
deliver more fluent-sounding translations. Neural Machine Translation system is
kinda a mixture between the first two systems, takes a little bit from both of
them and translates with great efficiency.
There are so many important things when it comes to translation
automation a.k.a machine translation. The reason why it’s called as both
translation automation and machine translation is that you can only automate
translation with machines. Since they both mean the same thing and you can not
actually automate translation without machines makes these important points
actually important for both of them. Also, the fact that most people use
computers to translate texts nowadays makes these important things even more
After all that,
there are two important things in machine translation; corpus and data
analytics. Corpus is a collection of linguistic data (usually contained in a
computer database) used for research, scholarship, and teaching purposes. And
it contains the data it needs to use for a specific purposed translation.
Corpus contains the data of what a writer’s most used words, what word groups
are commonly used in that area, how you can use them in your own text, which
word out of ten different words you can use for your text’s subject is more
viable than others, which words are more commonly used in your area of subject
and much more. It basically improves your translations quality by looking at
what other people or machines did before in the same subject as yours or
translates some of the worst before-hand so you don’t do that much translating
afterward which in both ways are greatly efficient and useful.
industry has come to a new era of data processing. This is a tendency that no
one can stop and at the same time means that people are more thoughtful about
past work and heritage. By constantly feeding the machine and interacting with
the machine through post-editing and manual data setting and annotation, people
produce systems that work with smarter machines. This includes Machine
Translation (MT) engines, terminology management platforms, translation memory
tools, translation management software, and multilingual annotation systems.
Translation-related data can be used widely in some specific areas and can help
translation automation in many ways. Translation-related data can support
machine translation in so many ways such as; supporting business
decision-making and help on making predictions. Translation-related data can be
used for business intelligence and help marketers solve business problems. The
business world aggregates, uses, and analyzes data to make more informed
decisions. Translation-related data can also help people to make predictions.
There are many data fields that can help us make predictions. For example, we
can analyze the translation patterns of an interpreter and foresee some quality
issues related to that person.
Finally, as we
can see from previous paragraphs data and corpus analytics are critically
important in translation automation. Data and corpus analytics are basically
what memories are for human beings, which is all the experience they gained
during their lifetime which again is critically important. (probably a little
bit more important than data analytics in machine translation.) Without data
and corpus analytics improving translation machines would be extremely hard
because even though you translated that same thing three hundred times before
it is not going to memorize until you say machine to do so. Also, translation
process would have been slow because the machine is not going to be remembering
what you just translated yesterday and not going to help you in any way. But
since we have data and corpus analytic systems available for us, they can teach
themselves up throughout the translate and help us in other translation jobs.
This is why data and corpus analytics are critically important in machine translation.