Machine translation is all about providing a computer or a machine with the task of transferring from one natural language to another. You can see how this is done in many different ways. Among those different ways, Example-based machine translation has received a lot of positive attention in the recent past.
It is possible for you to discover many different variations in machine translations. Out of those variations, Example-based machine translation has received a lot of attention.
What is Example-based machine translation?
Example-based machine translation is the process where translation takes place by analogy. Here, the system responsible for translations is provided with a set of sentences within the source language. Then the corresponding translations in the target language are also provided to the system. They work as examples for translations. Along with that, the system will be able to keep an eye on the examples and proceed with getting translation work done at the end of the day. This is an effective method of translating text from one language to another. Hence, you are strongly encouraged to pay attention to it.
You can find numerous sub-variations in Example-based machine translation as well. Translation memory is one of the most prominent forms of Example-based machine translation available. This form of translation is available to you commercially.
When it comes to a translation memory, you will be able to see how the user keeps on translating text. The translated content will be added to the database by the same user. When the system comes across the same sentence during the translation process, an example loaded in the database will be used as a reference to move forward with the translation process. That’s the main reason why we say that Example-based machine translation is effective. It is translating from one sentence to another instead of the word to word. Therefore, the chances of translated content ending up with mistakes are relatively high.
How does Example-based machine translation work?
Now you have a basic understanding of the functionality of Example-based machine translation. While keeping that in mind, let’s deep dive and see how Example-based machine translation works.
In most of the instances, Example-based machine translation takes place on the parse tree. Or else, it will look for the most similar sentence in the database and make modifications to the translation based on the differences that exist. Along with that, there is a possibility to translate the source sentences in an effective manner.
In order to proceed with Example-based machine translation, it is important to have a massive volume of translated content. In other words, the database should be loaded with numerous parallel bilingual texts. These texts have been translated from experts. The experts don’t just have language proficiency. They are experts in both languages as well. Hence, it is possible to make sure that the database used for Example-based machine translation is solid, and it can deliver high-quality results at all times.
Example-based machine translation is a process that is being used to decode knowledge out of bilingual texts. Here, the knowledge doesn’t seem to have any overt formal representation. It is not linked with any encoding scheme as well. Instead, you will notice that the knowledge is encoded in a straightforward manner with text coupling. The machine used for translation will take a piece of text, match it with a similar piece of text available within the database, and proceed with the translation.
One of the key objectives of Example-based machine translation is to proceed with translation work based on the data provided as inputs to the system. Then there is a possibility to end up with quality results at the end of the day.
In other words, fragments of source, including phrases, words, and many other non-constituent chunks, are available. While using that, it is possible to come up with the best possible translated piece of content in the target language. In addition to that, there is a possibility to go ahead with a large scale translation based on the availability of information as well.
Along with improving technological developments such as artificial intelligence, we can see how the accuracy associated with Example-based machine translation is increasing along with time. Therefore, you can take a look at Example-based machine translation as a quick method to get content translated from one language to another.
This article first appeared on Harry Clark blog