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Last updated on: January 28, 2025

What is Hybrid Machine Translation and How Does It Work?

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Technology, a boon or a bain is an argument that continues to dominate our life. Amidst all this noise, technologies dominating the content translation space continue to evolve at a break neck speed with machine translation and artificial intelligence (AI) led language technologies simplifying the content translation process. However, using machine translation often leads to translation inaccuracies resulting in loss of business and trust. Human translations though accurate and high quality are time consuming and hence not scalable.

 Hybrid machine translation combines several methods of machine translation in a single machine and in some cases human editing and vetting post translation. The ability to combine different translation methods and in most cases both machine and human translation have earned hybrid machine translation a sweet spot in the realm of translation technologies.

Before we delve into hybrid machine translation lets understand machine translation and human translation to fully appreciate the advantages of hybrid machine translation.

Machine translation

The translation is done from one language to another using a translation software without any human involvement. The translation quality varies and depends on the target language in which the source language is being translated. AI backed machine translation tools have the ability to process vast amounts of data, apply sophisticated algorithms and machine learning techniques for accurate translation making them apt for large translation projects. However, they miss the cultural context, subtleties,and idiomatic expressions, making them more robotic than human-like.

Types of Machine Translation

Rule-based machine translation (RBMT): One of the earliest forms of machine translation, RBMT translates content based on grammar rules. However, translation done using RBMT requires extensive human post editing as the output is often inaccurate. Since it is strictly grammar rule based the sentence formation often gets distorted when translated from one language to another.

Statistical machine translation (SMT): In this method first a statistical model of relationships between text words, phrases, and sentences is made. This translation model is then applied to a second language and converts the same elements to the new language. 

Neural machine translation (NMT): NMT harnesses the power of artificial intelligence to learn  languages and improve its accuracy. NMT mimics neural networks in the human brain which makes NMT translations more context aware and human-like making it a popular choice in  machine translation techniques. NMT works by training itself with two kinds of data, generic and custom.

  • Generic Data: Refers to the data learned from translations performed over time by the machine translation engine (MTE). This data produces a generalized translation tool for various applications, including text, voice, and documents.
  • Custom or Specialized Data: This is training data fed to a machine translation engine to build a custom translation tool for a specific industry or business. Banking, Healthcare etc. have some specific terminologies specific to their domain for which a separate  glossary is made.

Human translation

In this case a human translator proficient in both the source and target language meticulously goes through the source content and does a manual translation in the language requested. Human translators understand the cultural relevance, idiomatic expressions, and are able to deliver context-aware translations. The quality of human translations is better than machine translation as they understand the context that may be lost in machine translation. Also, when translating human translators tune the translated content to come across as more human like and resonate with the audience.

Hybrid machine translation

Utilises several machine translation methods such as rule-based machine translation, statistical machine translation, and neural machine translation inside one machine translation system and the expertise of human translators to get fast and accurate translation output. There are several hybrid approaches to machine translation. Research proves that using a single method of machine translation often leads to inaccurate translation resulting in wrong information being communicated. A hybrid approach to machine translation helps overcome this challenge.

 

Some notable hybrid machine translation examples are:

An ecommerce store serving customers across different states and countries can use hybrid machine translation to translate their website and app so that customers can easily navigate the store in their preferred language.

Similarly, companies operating in different countries can leverage hybrid machine translation to communicate with employees in their native language for better understanding.

Limitations of Machine Translation

While AI-led machine translation offers a myriad of advantages like translating large volumes of data quickly, real-time translation, and cost-efficiency it still fails to deliver accurate results. Some pressing challenges with machine translation are:

Lack of context: Unlike humans, machine translation lacks the ability to understand cultural context, expressions, etc which may result in a translated output which might be perceived as offensive in some cultures.

In 2018, Indonesia was hit with a deadly earthquake, and Facebook mistakenly ended up celebrating the occasion by adding confetti and balloons to the posts and updates around the earthquake. Many people took to social media and used the word “selamat” which means “be safe” or “to survive”. Facebook’s algorithms misinterpreted the messages and comments in the wrong context giving the tragedy a festive flavour.  

Lack of creativity: Machine translation is logic and algorithm based which means it lacks creativity, and the translated output can at times come across as robotic devoid of any emotion. Such messaging often fails to establish any connection with its audience.

Limitations of Human Translation

Lack of consistency: Different translators have their own approach, perspective, understanding of culture and style making consistency a challenge in large translation projects where multiple translators are involved. Maintaining consistency in terminologies throughout is possible in smaller projects with human translation though.

Time-consuming: Manually translating each word and sentence is time consuming and can impact project delivery timelines and add up to the overall cost of executing a translation project.

A Hybrid Approach

Both human and machine translation have their limitations when employed in isolation. By combining both one can leverage the capabilities of both human and machine translation to deliver translation services that can set a benchmark in the industry. This hybrid approach to machine translation uses machine translation as the foundation to translate large volumes of data quickly, efficiently, and ensures consistency in delivery. Human translators enhance these machine translated copies with their linguistic expertise taking into account cultural nuances, idioms, expressions, and setting the right context.

Hybrid machine translation expedites project delivery timelines, keeps project costs in control, and is a scalable model making it perfect for both SMBs and large scale enterprises. Reverie’s Anuvadak is powered by a neural machine translation engine expediting context aware translations in multiple languages both Indian and foreign. It effortlessly integrates new content by detecting and sending it for translator review, maintaining a current and coherent multilingual web presence. With customised solutions for various industries Anuvadak is one of the best AI-driven hybrid machine translation platforms in the industry.

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