Hybrid Machine Translation and its Various Approaches


Translators adopt various translation approaches to complete their tasks. Out of those translation methods, hybrid machine translation has received a lot of attention in the recent past. This method aims to overcome the limitations of human translation methods, and it combines the main aspects of multiple translation approaches to improve fluency, accuracy, and precision. Let’s discuss hybrid machine translation and its various approaches in detail:  

What is Hybrid Machine Translation? 


Hybrid Machine Translation (HMT) is a dynamic process that incorporates aspects from various other translation processes to get the job done. Machine translation has significantly improved in recent years. Various approaches have been introduced in the field of Machine Translation including Statistical Machine Translation, Neural Machine Translation, and Rule-based Machine Translation. Hybrid means mixture, therefore, Hybrid Machine Translation includes the combination of the above-mentioned approaches to improve the translation quality and address the respective limitations. 


Research proved that a single translation system was often less effective. It did not provide a high-quality accurate translation. As a result, there emerged the need to come up with a new and accurate translation mechanism. That’s where hybrid machine translation came into play. It could overcome the issues that appeared in single translation methods.  


With that understanding in mind, let’s deep dive and take a look at some of the most prominent hybrid translation methodologies that are being used as of now.  


Types of Hybrid Translation Methods

Statistical Rule Generation 

This type of hybridization involves statistical systems and rule-based translation systems. Rule-based systems are based on linguistic rules and dictionaries. They have good command over grammar but they struggle with idiomatic expressions. Whereas statistical systems have a strong command over idiomatic expressions, however, they lack grammatical accuracy as they analyze huge bilingual corpora to identify the best translations. 


In this hybrid translation approach, the rules of RBMT are combined with statistical data to get the best results in translation; statistical data is used to generate syntactic and lexical rules. The input is then processed along with the assistance of the RBMT rules. 


This approach can avoid the time-consuming and complex tasks of creating a set of fine-grained and comprehensive linguistic rules and extracting those rules via a training corpus. 


With various benefits, this approach still has a few limitations. If you are about to try it, you are encouraged to have a basic understanding of these issues as well. Most of the issues that the statistical rule generation hybrid translation method displays are inherent in the basic principles that create it. For example, the accuracy of the translation heavily depends on the similarities that exist between the text contained in the training corpus and the input text. That’s why the statistical rule generation hybrid translation method was able to achieve a high level of success in some of the domain-specific applications. While it did not provide perfect results in a few other applications.  

Multi-engine Translation 


The multi-engine translation approach to hybrid machine translation is associated with running multiple machine translation systems in parallel with each other. The final output is usually generated by the combined output of all the sub-systems involved in the process. Techniques like voting, weighted averages, or neural network-based methods are used to merge the outputs and generate reliable and accurate translations. 


Various mechanisms incorporated in this approach assess fluency, appropriateness, consistency, and domain relevance. Usually, the systems that use multi-engine translation methods are rule-based and statistical. However, the other combinations are explored as well. A group of researchers which came from Carnegie Mellon University was able to end up with some success when they combined transfer-based, example-based, statistical, and knowledge-based translation sub-systems into one single machine translation system. 


The muti-engine translation is one of the most widely used hybrid translation methodologies in today’s world. Depending on the working mechanism, it can provide highly effective results to the translators while completing their projects. Multi-engine translation considers perspectives from diverse engines therefore, it increases robustness against translation errors and peculiarities.  


If you are a translator, you are encouraged to take a look at multi-engine translation before you dive into the various hybrid translation methodologies. 


Multi-pass Translation 


Multi-pass is another popular hybrid machine translation approach. This approach involves multiple stages to improve the quality of translation; the input is processed serially multiple times. 


  • During the initial translation phase, the source text is analyzed using a primary translation engine such as Statistical Machine Translation which provides a baseline translation that might contain errors and inconsistencies.

  • After that, the product is analyzed in the second stage to identify errors and needed improvements. In the second step, human translators access the translation for fluency, accuracy, and semantic coherence.

  • After the analysis comes the re-translation and then the post-editing phase. In the re-translation stage, a different translation engine is used to generate an alternative translation that will address all the identified issues. While in the post-editing phase, human translators play a significant role as they manually edit or refine the translation to improve accuracy, fluency, and overall quality of translation.

  • Last comes the consolidation phase. During this phase, outputs from the above-mentioned stages are combined either manually by human translators or through automated systems. Various techniques like voting, ensemble methods, and quality estimation mechanisms might be incorporated to consolidate the best parts of each phase. 


The multi-pass translation technique is widely used to limit the extent of information that a statistical system should consider. This will significantly reduce the processing power needed as well. On the other hand, it removes the need to have a rule-based system to complete a translation for getting a document translated from one language to another. Also, this translation approach is positioned to significantly reduce the human labor and effort that is needed to develop a system.


Apart from the above-mentioned hybrid translation approaches, many other approaches are being used and tested. However, these three approaches hold a prominent place out of those approaches. 


Final thought!


Hybrid Machine Translation is a dynamic and promising approach that incorporates various translation techniques to overcome the shortcomings of an individual translation method. Its potential to improve fluency, accuracy, and semantic or context comprehension makes it a vital instrument in suspending language barriers, generating refined translations, and enabling global communication.

Also, read The Profession of Translators: Pros and Cons