How Effective is Machine Translation for African Languages?

Machine Translation is a branch of automatic language processing concerned with translating the language into other languages using digitization to facilitate tasks for a group of non-native speakers.

It is defined as the intervention of artificial intelligence by helping the computer perform the act of translation through the linguistic and cognitive patterns stored by the structures’ action.

And terms it takes back in exchange for the language from which it is translated.

Machine translation is an automated translation by computer softwares. Users insert text in their source language and then select their target language. The auto engine then generates the wanted translation.

Speed of Machine Translation

Machine translation can do translation of large volumes of text pretty quickly, which then would be close to impossible using traditional translation methods.

How Effective is Machine Translation?

• It’s fast
• It’s scalable
• It’s cost-effective

Lower Costs

This can help you save money when you don’t necessarily need to hire a human translator.  

How does machine translation work?

Today, translation technology is advanced and continuously improving. There are several different types of MT approaches, such as rule-based, statistical, example-based.

Neural machine translation

Still, typically, the web can be divided into two components: an encoder that reads the input sentence and generates a representation suitable for translation and a decoder that generates the actual translation.

SMT only evaluates the fluency of a sentence a couple of words simultaneously, whereas NMT evaluates fluency for the entire sentence.

Use of machine translated data

Machine translation works on training data. The data can be generic or custom, depending on your needs.

What’s the future of machine translation?

However, many ever-changing options can make machine translation more challenging to access for newcomers and optimally leverage for existing users.

Machine translation quality: Is MT good enough? How do I know?

Although significant advances have been made with MT, there are still some doubts about the translations’ quality, which make some users hesitant to invest more into MT.

In contrast, others are unsure how to effectively scale their evaluations to match the output volume that MT can produce.

How do I implement machine translation?

Once you have a strategy in line, you should start to think about implementing. Also, adding a professional provider of translating and interpreting service to your localization workflow doesn’t have to be a daunting task.

Simply follow these implementation steps –

• Pick the right content for machine translation.
• Run samples before deployment to get an idea of the quality or identify areas that could be improved before deployment.
• Deploy! Keep in mind the results may not reach your expectations right away, but the outcome will get better over time.

When should you use machine translation?

Using machine translation depends on several factors.

Audience

Who is it for? Regardless of your use case, it would help if you were sure that the machine translation output would meet the reader’s expectations. A human should certainly review content that is showcasing your company or product.

Volume

Are you translating small segments here and there? Sure, you can use MT.

Content priority

Content that is of low priority, such as internal documentation, or has a short life-cycle is a perfect machine translation candidate.

Definition of machine language processing:

It is a science concerned with processing language by computer in a scientific way based on an algorithm’s principles.

It is known that it is a marriage between two sciences of language and computer, one of which is no less important than the other.

This science belongs to the category of cognitive sciences by interfering with artificial intelligence.

He said: “Study computer systems to understand natural language generation according to a scientific computational perspective.

The study of local languages and their processing has been around for more than hundreds of  years and it grew out of the field of linguistics experts with the rise of trade and technology.