Exploring the Role of Machine Translation in Natural Language Processing

Exploring the Role of Machine Translation in NLP


Machine translation is the process of automatically translating documents from one human language to another while retaining their context and producing information that is fluent in the target language. Despite machine translation being among the earliest subspecialties of AI study, the current shift into large-scale empirical approaches has resulted in considerable advances in translation quality.

Importance of Machine Translation for Global Business

Global business demands not only navigating unfamiliar markets but also linguistic barriers. With the help of Natural Language Processing (NLP), it is possible to automatically and accurately translate between two languages.  It provides the most viable alternative for a quick and effective translation process that does not require human intervention.

In this blog, we’ll go into precise detail about machine translation in NLP, including how it works, what it can do for you, where it can be used, and several other aspects.

How Does Machine Translation Work?

More complex translations can be performed using corpus methodology, taking into consideration improved handling of differences in phonetic typology, expressing acknowledgment and translations of phrases, as well as the segregation of oddities. Although some technologies can’t now work as well as a human translator, this will be attainable in the future.


The leading machine translation technology developers—Google, DeepL, and Amazon—all employ a sort of neural MT as their preferred methodology since it permits more complex translation while also adding additional language combinations. This feature is enabled by the potential of MT translation software to evolve and improve when it is used more frequently.

In localization, translation quality is a popular subject. But what does the word “quality” imply to you?

You may say “it works” or “it is effective.” Someone else could describe it as “well-built.” Another person would contend that quality equals perfection. You get the idea. In reality, quality involves achieving equilibrium between resources and outcomes while meeting standards. Keeping this in mind, the three best translation software are:

1. Google Translate

It is generally regarded as one of the top machine translation tools, taking into account usability, language support, and search integration. It has received a terrible rap in the past, and that’s not totally fair. While you should never rely entirely on Google translations, it does provide an easy and inexpensive approach to translating digital content into more than 100 languages.

2. DeepL

It is a well-known program that offers neural machine translation in 29 languages. The translations frequently sound more natural than other MT alternatives. You can always switch between DeepL and Google MT to discover which choice best matches your needs.

3. Amazon Translate

Amazon Translate is similarly neural-based and linked closely with Amazon Web Services (AWS). However, it’s crucial to remember that every one of these engines has always been evolving when making parallels in translation technology. Some studies reveal that Amazon Translate is much more effective with some languages – most prominently Chinese.


Depending on how you program the commands into a machine translation model, whether it is with recurrent neural networks or supervised and unsupervised, the translation will be classified into one of four categories.

1. Rule-Based Machine Translation (RBMT)

This kind of automated translation starts the translation process by collecting grammatical data about the source and target language documents. It is mostly utilized in online dictionaries and grammatical checkers. Three different sorts of analytical techniques would be used by a rule-based algorithm to translate. Such as: an English sentence to Italian:

  • Morphological: Analysis of the shape and structure of the sentence
  • Syntactic: Takes into account the language principles being used
  • Semantic: Analysis of the sentence’s basic essential meaning

2. Neural Machine Translation (NMT)

NMT is a type of machine translation that uses neural network simulations to develop predictive methods with the end objective of translation. The major benefit of NMT is that it provides a single solution that can be configured to unwind both the source and target text. As a result, it does not rely on specialized mechanisms that are common in other MT systems. On the other hand, NMT primarily supports machine translators in achieving a specific objective.

Following successful translation, the system improves its competence, eliminating errors each time. With its neural network-based approach, the translation device is designed to mimic the workings of the human brain.

3. Statistical Machine Translation (SMT)

It operates by relying on statistical models that are based on the analysis of massive amounts of bilingual data. It predicts the connection between both the source language and a target language. GOOGLE TRANSLATE is a real-world example of this.

One significant drawback of the SMT approach is that it can only translate a phrase if it is present in the reference texts that have been provided to it prior.

4. Hybrid Machine Translation (HMT)

The HMT technique entails a single system that simultaneously integrates multiple independent machine translation procedures. Multiple translation systems operate in parallel, generating a synthesis of all subsystem outputs. HMT techniques include multi-engine, quantitative guidelines, multi-pass, and confidence-based.


Human translators employ machine translation services to translate more quickly and efficiently. The following are some of the pros of machine translation:

  • Gives a solid starting point for expert human translators
  • Operates incredibly rapidly, translating billions of words almost instantly
  • Provides assistance in 50–100+ languages
  • Enhances productivity and the capability to deliver translations more quickly, leading in a relatively short time to market
  • Delivers basic but significant translations, lowering both delivery cost and time
  • Analyzes foreign language texts, looks for appropriate words, and retains those terms for potential application

Our Take

Natural Language Processing gives us the ability to transmit speech and textual information to machines, as well as retrieve massive volumes of this data in a systematic manner. It still hasn’t reached full development or delivered its finest.

A variety of techniques can still be incorporated into applications. Very few BI and analytics providers are delivering NLP capabilities, however, they are currently in the minority. More software will be integrated into even more systems and products in order to continue to compete.

As it becomes more common, NLP will enable people to communicate with more technology in ways.  On the other hand, this device-human-NLP increased collaboration will enable customization and advancements in a broad array of disciplines. If you are looking for NLP-based solutions to revolutionize your business, the expertise of Futuristech in NLP software solutions can assist you.


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