Out of the different translation methods available out there in the world, machine translation can be considered as the quickest approach. That’s because machine translation is fully automated. During the machine translation process, a computer software is being used in order to get a piece of content translated from one language to another.
For a translation to be effective, the meaning given out by the text in original language should match perfectly well with the meaning given by text in the translated language. However, machine translation is not fully capable of doing this. To overcome this issue, people have been introducing a variety of complex procedures into machine translation software. However, machine translation still cannot guarantee a 100% positive outcome for the translators.
Translation cannot be considered as a straightforward job. In other words, a translation should not be a word for word substitution. If that approach is followed, there is a high possibility of ending up with inaccurate results. To get an effective translation job done, a translator should interpret and analyze every single element that exists in the text. Then it will be possible to understand how the words in the text are influencing other words. To get the job done, a translator should be an expert in semantics, syntax and grammar. In addition, the translator should be extremely familiar with the new language as well.
Machine translation methodologies that are being used in today’s world can be divided into two different categories as rule-based machine translation and statistical machine translation. Let’s deep dive and take a look at these two approaches.
Rule-based machine translation is powered up by a large number of inbuilt linguistic rule. In addition, thousands of bilingual dictionaries that contain each language pair are used to get the job done.
A rule-based machine translation software would parse the text and come up with a transitional representation of the targeted language. This process is in need of extensive lexicons, syntactic and semantic formations. In addition, a large number of rules are being used to get the job done. The software that uses the complex set of rules would then transfer grammatical structures of the input language into the targeted language.
The rule based translation approach depends on massive dictionaries. In addition to that, a large number of linguistic rules are also being used. The users are now provided with the opportunity to improve the out of the box translation quality by including their own terminology to the process of translation. In other words, the translators will be able to create their own dictionaries, which can be used to override the default dictionaries. This can improve the efficiency of the results that you will get from a rule based translation approach.
In most of the instances, two different steps are linked with a rule based translation system. The first step would be the initial investment. It has the ability to increase the overall quality of the translation at a limited expense. Then there would be an ongoing investment in order to improve the quality of the translation. This will improve the translation quality incrementally.
Now you know what rule based machine translation is all about. With that in mind, let’s go ahead and take a quick look at statistical machine translation technologies. The statistical machine translation is using statistical translation models, where the parameters come out from the analysis of bilingual and monolingual corpora.
The steps that have to be followed in order to develop a statistical translation model can be done within a short period of time. However, the translation approach heavily depends on the existing multilingual corpora. To get positive results out of this translation approach, at least two million words for a single domain should be available.
Due to this reason, there is a possibility to achieve a quality threshold when proceeding with statistical machine translation approach. However, most of the companies that exist out there in the world don’t have such massive multilingual corpora, which is needed to develop the translation models needed. On the other hand, the statistical machine translation methods are extensive and they require a lot of processing power. To cater to those demands, an extensive amount of hardware configurations are also needed to run the translation models.
These are two of the most prominent machine translation methods that are being used in today’s world. Even though these translation methods can deliver quick results within a short period of time, it is not being used frequently because they cannot guarantee 100% accurate results in all the applications. In the future, we will be able to see it doing really well, but still, the things are not developed up to that level.
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