Machine Translation Customization vs. Machine Translation Training: What’s the Difference and Why It Matters?

With the constant evolution of multilingual content and AI enabled communication, machine translation (MT) has become an integral part of the creation process. However, organizations frequently express the desire to improve quality, nuance, and domain relevant translation quality. This usually expresses itself in two ways: MT customization and MT training.  

It seems like a very similar space. They both elevate the value of machine translation. But they are quite different forms of elevation in terms of methods, impact, scaling, and usage. 

This blog explores the difference and why it matters should you wish to localize your content at scale while wishing to maintain nuance and brand voice. 

Contextualizing the Issue: What is Machine Translation?  

Machine Translation is the automatic translation of content from one language to another language (most often) using AI models. There are a number of popular MT engines (Google Translate, DeepL, Amazon Translate, Microsoft Translator) with general, pre-trained models aimed at some degree of content.  

That said, the typical MT is unable to translate effectively for combinations like:  

• Industry references. 

• Brand references for voice or tone. 

• Unusual language pairs or dialects. 

• Sensitive or regulated content. 

 

That's where customization and training come into play. 

 

What is Machine Translation Customization? 

It is the process of modifying or changing an existing pre-trained MT model to better suit specific brand or domain needs.  

This does not mean training the model again from scratch as traditional training does. Instead, it involves putting additional knowledge or preferences on top of the base model. Let’s explore how it works: 

 

• Terminology injection: The addition of glossaries or preferred translations for key terms. 

• Style tuning: The fine-tuning of levels of formality or preferred sentence structure (i.e. casual versus technical).  

• Contextual adaptation: Provide example translations (translation memories) to show preferred outputs. 

• Post-edited feedback loops: Refinement of translation quality through human feedback without changing the model's underlying architecture.  

There are plenty of commercial MT providers that allow for customization: 

• Amazon Translate Custom Terminology. 

• Google AutoML Translation (for light fine-tuning). 

• Microsoft Translator Custom Translator. 

• DeepL Glossary. 

The Benefits and Limitations of MT Customization 

Benefits 

• Quick to implement. 

• Low dataset requirements. 

• Cost-effective. 

• Great for marketing content, FAQs or helpdesk documentation.  

Limitations

• Limited effect on deeply syntactic or semantic errors. 

• No significant gains for highly technical or domain-specific tasks. 

• Limited to languages that are not long-tail or brought from noisy (ungrammatical) source data. 

While customization tweaks the engine’s behavior, training goes deeper, building a translator from the ground up.

What is Machine Translation Training?  

Training, in contrast, is the process of training a translation model from scratch (or from a foundational multilingual model) with a large bilingual corpus that fits the domain. It is a more involved and intensive process because it requires more data, but it can be much more powerful and useful.  

Let’s see how this works: 

• Collecting bilingual parallel corpora—pairs of aligned source and target languages. 

• Pre-processing, tokenizing, and cleaning the data. 

• Ingesting the data into an MT architecture (e.g., Transformer models like MarianNMT, OpenNMT, or custom LLMs). 

• Running training for several epochs, with a validation quality step using BLEU, TER, or human evaluation. 

• Fine-tuning, or re-training regularly with newer datasets. 

Use Cases: 

• Translations of legal, medical, or scientific documents that contain domain specific minimalist jargon. 

• Building MT for lower-resourced languages or dialects. 

• Construction of multilingual AI assistants or content platforms in non-existent controlled environments. 

• Government or enterprise use cases where security and the need to control in-house language are non-negotiable.  

The Benefits and Limitations of MT Training 

Benefits

• Absolute best translation quality for specific niche use cases.  

• Control over tone, terminology, and bias reduction.  

• Robustness across different input situations or in noisy inputs.  

 

Limitations

• It requires huge bilingual datasets (generally in millions of sentence pairs).  

• Requires powerful computing and advanced ML expertise (most processes take weeks at a minimum).  

• The costs associated with regular maintenance and updating are expensive. 

• It can take weeks or months to deploy as the process takes a long time to train.  

Now that we’ve covered both, Machine Translation Training and Customization, here’s a side-by-side comparison to help you decide which approach suits your needs better.

Comparison Table: MT Customization vs. MT Training 

 

Feature	Customization	Training
Data requirement	Low to medium	High (millions of sentence pairs)
Time to deploy	Days to a few weeks	Several weeks to months
Technical expertise needed	Moderate (linguistic expertise)	High (ML engineering, NLP)
Cost	Lower	Higher
Control over output	Limited	Full
Use case suitability	General + moderately specific	Deeply domain-specific
Language coverage	Limited to provider-supported	Can be trained for rare languages
Integration ease	Plug-and-play	Requires custom pipelines

Real-World Scenarios

Customization Example 

Imagine you run a global e-commerce company. You need your helpdesk chatbot to translate common customer support queries from English to Spanish. You inject the preferred translations for product categories and adjust the tone to remain friendly but professional. Your team takes advantage of Amazon Translate's Custom Terminology to reduce mistranslations and also improve client satisfaction without needing to retrain a full MT model. 

Training Example 

Your pharma company needs to translate clinical trial materials from Japanese to German. Although, the problem is that due to the presence of highly technical words, the translation outputs from standard MT engines are of significantly low quality. Your company then trains its own NMT model from 5 million bilingual sentences from previous materials to significantly increase output quality that is acceptable for regulatory filings. 

Customization or Training: Which Should You Choose? 

Choose Customization if you: 

• Currently use a commercial MT engine. 

• Have light domain needs. 

• Require a faster implementation. 

• Need to scale marketing or customer support content. 

• Have a medium-sized budget. 

Choose Training if you: 

• Need high-stakes accuracy (eg: legal, medical). 

• Are presenting sensitive, proprietary, or technical data. 

• Need to support low-resource or input languages. 

• Want complete control of outputs and refreshes. 

• Are building in-house NLP products or multilingual apps. 

The Hybrid Future 

The future of MT is likely hybrid, starting with an off-the-shelf engine, customizing that engine with internal knowledge and training domain-specific components if required.  

For example, as systems adopt LLMs into their MT process, there could be potential to use prompt-based customization and use zero-shot learning without additional training.  

Organizations that take a strategic approach to MT - investing effort based on use case - will deliver better (humanlike) multilingual experiences and at the same time drive down translation costs. 

Final Thoughts 

Machine translation is not a one-size-fits-all solution. The decision about whether to use customization or training is based on domain complexity, availability of data, expectations of quality and the scalability goal of the organization.  

By understanding the differences and applying use cases, organizations can align multilingual approaches with practical limitations and long-term aspirations, realizing the best potential of language technologies driven by AI.