Introduction

From a common point of view, translation is not just a literal substitution of a word rather, and it is a process of interpreting and analyzing the word or sentence and giving an output of how each word influences the other. Machine Translation, also abbreviated as MT, is an automated process of translating one language (like French) to another (like English) using any computer software. With the increase in the change in the business environment, businesses are using MT to complete and fasten their translation process more than ever.

On looking into the history of MT, it became a reality in the 1950s; however, few references to this subject can also be traced back to the 17th century. And nowadays, there are various MT examples which are from various fields like legal, financial, military, defense, etc. Not only this, there are many applications of MT as well, which include categories like text to text, text to speech, speech to text, speech to speech, image (of words) to text. 

In more technical words, Machine Translation can be defined as a form of research of linguistics done through the software over a computer, and it and further translates any kind of sentence or text or even an image (of words) from one language to another. It has its own set of benefits which include an increase in overall efficiency in the business, translation costs are reduced, and time to markets also gets reduced, which increases not only the productivity but also the terminology consistency. It sometimes works better where the content is repetitive and simple.

If we compare machine translation vs human translation, then on average, MT can produce 8000 translated words in comparison to 2500 words with human translation and also helps to produce more translations to customers with greater speed. There are four types of MT which include- Rule-based machine translation technology, Statistical machine translation technology, Hybrid machine Translation, and Neural Machine Translation. The most common types of MT used are Rule-based Machine Translation and Statistical Machine Translation which are further described as follows:

In this article let us look at:

  1. Rule-based Machine Translation Technology (RBMT)
  2. Statistical Machine Translation Technology (SMT)
  3. Rule-based Machine Translation vs Statistical Machine Translation

1. Rule-based machine translation technology (RBMT)

The Rule-Based Machine Translation uses unlimited built-in linguistic rules, which further breaks the sample and hence produces a more predictable output for terminology and grammar through the use of customized terminology lists. Errors can be rectified easily as Rule-Based MT engines don’t need a very diverse range and structural range of texts, not even digitized data. There are three types of RBMT which are defined as follows – 

  • Direct systems – It maps input to output with basic rules.
  • Transfer RBMT systems – It includes morphological and syntactical analysis.
  • Interlingual RBMT systems – It uses an abstract meaning towards the texts.

2. Statistical machine translation technology (SMT)

Statistical Machine Translation Technology uses statistical models and completes the translation process from the source given by the customer. They don’t comprehend text based on language rules, but they built a statistical model is built by analyzing bilingual corpus. It is CPU intensive which requires a considerable amount of hardware to run.

3. Rule-based Machine Translation vs Statistical Machine translation

Rule-Based Machine Translation:

  • It produces a more consistent and predictable quality.
  • It may produce an out-of-domain translation quality.
  • It knows the grammatical rules.
  • The quality of performance is decent.
  • There is a consistency between the versions of translations.
  • The cost of development and customization is high.

Statistical Machine Translation:

  • It produces an unpredictable translation quality.
  • Its out-of-domain translation quality is poor.
  • There is a higher requirement of CPU and disk space requirements.
  • There is an inconsistency between the versions of translations.
  • It is cost-effective, and the development cost is high.

They both differ in the way they process and analyze content that is often combined within the same system and known as hybrid Machine translation.

Conclusion

As technology is advancing MT system has evolved with significant improvements in its availability and its ability to support the traditional translation process. MT has enabled greater efficiencies in the traditional translation process with productivity gains as high as 130% for certain languages. However, there are still some problems of MT which include various quality issues, inability to receive feedback or collaboration, translating sarcasm, understanding multiple meanings of translation, and so on. RBMT and SMT both have some disadvantages; therefore, it is a better idea to go for a third approach that has its own decent set of advantages.

Big internet companies like Google, Microsoft are exploring the feasibility of neural networks. Neural networks are a type of statistical learning model that was initially utilized in image and speech recognition technology. Its application in MT is similar to the way that the human brain works, which is through the method of trial and error, this is also called the method of deep learning. It might be early to assess the future potentiality of this type of MT, but it is already evident that it will be a more fluid form of translation. Several language pairs on Google Translate, and Microsoft translator has already switched to Neural Machine Translation.

There are no right or wrong ways of learning AI and ML technologies – the more, the better! These valuable resources can be the starting point for your journey on how to learn Artificial Intelligence and Machine Learning. Do pursuing AI and ML interest you? If you want to step into the world of emerging tech, you can accelerate your career with this Machine Learning And AI Courses by Jigsaw Academy.

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