Mastering Machine Translation: Customization vs. Training for Content That Wins

More businesses lean on Machine Translation (MT) every year, and adoption may climb faster than analysts forecast. Mordor Intelligence projects the Machine Translation Market to grow at a CAGR of 11.69% between 2026 and 2031.

In under half a year of running a translation memory system, the digital marketing agency Dataduck was able to auto-translate 15% of its content through TM alone, which cut per-word costs sharply (Webincare). Two forces drive the shift: results from the technology keep getting more dependable, and teams face steady pressure to ship more content in many languages without growing the budget. MT moves faster and costs less than people can match on their own, yet quality gaps remain a real concern. Winning in digital markets means delivering multilingual content that fits the subject, lands the right tone, and keeps one brand voice everywhere it shows up.

Getting the Most From Machine Translation

Ready to sharpen your MT program and reach your targets? There are two routes to stronger Machine Translation output: customization and training. Both lift translation quality and shrink the post-editing workload. Even so, the two are not the same thing, and one cannot stand in for the other.

Below, you will see how each method functions, where they differ, and how to pick the one that fits your situation.

Where Generic MT Engines Fall Short

For broad, everyday text, off-the-shelf engines like Amazon, Bing NMT, DeepL, Google NMT, or Yandex often do the job well enough. The catch is that the results do not always hold up.

What goes wrong? A generic engine can stumble on dense, field-specific material in areas such as life sciences or law, along with the vocabulary unique to those fields. It may pick the wrong sense for a word that carries several meanings. It also tends to flatten your brand voice and miss the moments when formal or casual phrasing would connect better with readers.

Customization and training close those gaps, lifting translation quality to meet needs that plain engines simply cannot reach.

What MT Customization Really Means

Customization tunes an engine you already have by feeding it a translation glossary and a Do Not Translate (DNT) list, which sharpens the accuracy of the output. (A glossary holds a company's must-have terms and their approved translations, and a DNT list names the terms the company wants left alone.)

Here is how it runs: you load a set of source terms and their translations into the engine before it begins. That list tells the engine how to render certain terms, or to leave others untouched on purpose. The suggestions get sharper as a result, which helps you protect your brand name, hold to set terminology, and handle regional differences. Cleaner output means fewer trips through post-editing.

The Tricky Parts of MT Customization

Customization is usually quicker to set up than training, yet a few hurdles show up along the way. Loading terms into the system can be straightforward, but knowing which terms belong there is the hard part. How well it works rests on the expert behind it and their command of input and output normalization rules, DNT lists, and glossaries, all of which shape the final quality. A less experienced author can quietly push the engine toward weaker suggestions and drag the whole translation down.

A Closer Look at MT Training

Training means building and teaching an MT engine with large volumes of bilingual data drawn from corpora and Translation Memories (content you have already translated), all to raise the accuracy of what the engine produces.

When you feed a generic engine your own bilingual corpora, it picks up how your company expects translations to read. Instead of handing back a one-size-fits-all suggestion, the engine shapes its output around your data. That lets you steer toward a particular brand voice or style and keep results consistent across projects. You can also dial back the stiff, formal default of generic engines toward something more relaxed. As with customization, you end up post-editing less, since a well-trained engine is more likely to land accurate translations with few slips.

To train effectively, plan to supply at least 16,224 unique bilingual segments that are high quality, free of inconsistencies, and clear of duplicated source translations. Skimp on data and you will likely see little or no gain in output quality.

How MT Customization and MT Training Differ

Both routes raise output and trim post-editing, yet they are separate methods, and one will not substitute for the other.

Customization adjusts an engine you already run by way of glossaries and Do Not Translate (DNT) lists. Training, by contrast, builds and teaches an engine from the ground up with large bilingual data sets pulled from corpora and Translation Memories.

Customization bends to more situations than training does, and its suggestions fit what most companies need. Keep in mind it carries a one-time charge to refresh the profile sent to the engine, plus a steady cost to keep the glossary current.

Training shines for companies with deeply specialized material and knotty use cases. Standing it up means paying for that first round of training, and possibly more later if performance checks point to room for gains.

Choosing Between MT Training and MT Customization

Do you need to translate scientific texts or dense technical manuals? Does holding on to your exact brand voice matter? How you answer points you toward the better fit, whether that turns out to be customization or training.

When MT Customization Is the Right Call

Two situations make customization the smart pick when your goal is:

• Exact rendering of specialized terminology
• Regional language differences, such as English (United States) against English (United Kingdom), when you lack enough data to train

Customization fits technical, detail-heavy content well, since getting terminology exactly right is what counts there. Reach for it whenever you do not have the data volume that training would require.

When MT Training Makes Sense

Two main situations point to training when your goal is:

• A clear brand voice, tone, or style while keeping post-editing low
• Regional language differences, such as French (Switzerland) against French (France), when you do have enough data to train

Training is a strong match for marketing and creative work, where holding a specific brand voice, tone, and style really matters. Just be sure you have the data volume needed to train the engines well.

Why a Hybrid Approach Can Win

Sometimes blending both methods delivers the strongest results. For instance, an engine may return better suggestions when customization rides alongside training.

ABC Translations helps customers put a hybrid approach into practice through its enterprise MT platform, Smart MT Portal. Customers can also tap professional training help from ABC Translations' experienced teams. Working with those teams, companies tend to land on a fuller MT strategy that puts both training and customization to use for stronger output. Various trials reveal which tactics perform best, so the MT plan fits the work at hand.

Weighing MT Customization Against MT Training

The best path to better MT output comes down to what you actually need. It is easy to treat training as the go-to answer or to get caught up in the hype around continuous training. Keep these points in view as you weigh your choices:

Avoid Pitfall #1: Pitching MT Training as the Only Answer

MT Training: Aim It at a Real Problem

Training can do a lot to lift MT output, but it only pays off when it targets a specific, well-defined issue.

As MT spreads, plenty of providers reach for training as their main way to deliver value to clients. That bet can misfire. Some companies that relied on training alone to raise their MT output later came to ABC Translations for help, frustrated once they crunched the numbers on its return. The engine's suggestions let them down, and they wanted something easier on the budget. The root of the letdown? For their particular needs, a different method would have served them better.

Forward-thinking providers like ABC Translations bring in training when it earns its place, but lean heavily on customization to reach the desired MT results for less than training would cost.

Avoid Pitfall #2: The Pull of Continuous Training

As you look at MT options, some providers will push the notion of continuous training that keeps running after each project wraps. Take such promises with care. Continuous training only makes sense for custom engines that genuinely need ongoing updates.

It bears repeating that real MT training calls for at least 15.6K unique segments to teach the engine well on a single project. When a company comes up short on data, it may fall back on project content to update customization features, a move that often gets mislabeled as "training."

What It All Comes Down To

Customization gives you a more flexible answer than training, and it fits what most companies are after. With it, you can lift MT suggestions a good deal while protecting your brand identity and sticking to set terminology. That eases the load on post-editors who would otherwise have to check those details. The single charge to update the engine's profile and the running cost to maintain a glossary usually come in well below what training would run you.

Best Practices for MT Customization

To get the most out of customization, lean on a few proven habits.

Input and Output Normalization Rules

Build a library of input and output normalization rules for the languages you use most, so you can shape what goes into the engine and improve what comes out. These rules let you hit requirements that are specific to you.

As an example, an input rule might tell the engine to use las comillas angulares [« … »] in place of double quotes ["…"] for Spanish output. That choice raises the quality of Spanish translations, since Spanish-speaking readers look for las comillas angulares rather than straight quotes. The same kind of rule lets businesses handle regional differences within a parent language, such as Spanish of Spain versus Spanish of Mexico or Argentina, and so on.

Do Not Translate Lists and Rules

Build a list of terms you want kept in the source, then set a rule that swaps each flagged Do Not Translate (DNT) term for a token before it reaches the engine. That hides the term from the engine and blocks any translation of it. After the engine returns its suggestion, an output normalization rule swaps the token back for the original DNT term.

Building Your Glossary

Give your glossary the care it deserves so translations stay accurate and consistent. Weigh the factors in Table 1 each time you decide whether a term earns a spot.

General Guidelines for Building a Glossary

• Frequency How often does the term show up in the source text? Leave out terms that appear rarely.
• Ambiguity Could it be read more than one way or be confused with other words? Add ambiguous terms, but confirm that other meanings rarely turn up in the source.
• Specialized terminology Is the term tied to a particular field or subject? Add domain-specific terms.
• Consistency Has the term always been translated the same way before? Leave out terms already handled consistently.
• Importance Does the term carry weight for grasping the overall meaning? Add terms that are central to the meaning.
• Complexity Is the term intricate enough to trip up the system? Add complex terms the MT system may struggle to translate correctly.

Table 1. Key Factors to Weigh When Building a Glossary for Machine Translation

Do's and Don'ts

Here are the do's and don'ts we strongly suggest following as you build a glossary (examples shown in Spanish):

• Don't add conflicting terms: steer clear of listing "banco" as both "bank" and "bench" in one glossary.
• Don't add duplicate entries: make sure "ordenador" and "computadora" are not both listed for the same sense, "computer".
• Don't add generic terms: skip everyday words such as "casa" (house) or "comer" (to eat), which can hurt translation quality, sentence structure, agreement, and word order.
• Don't break apart long terms: keep multiword phrases such as "redes sociales" (social networks) together as a single entry.
• Do add DNT (Do Not Translate) terms: include items like brand names, for example "Coca-Cola", that should stay untranslated.
• Do add specific product names: list product names such as "iPhone 13" or "Samsung Galaxy S22" in the glossary.
• Do keep one entry per source language: pick a single translation for "computer", either "ordenador" or "computadora", based on your audience's regional preference.
• Do use multiword phrases: add expressions such as "atención al cliente" (customer service) or "diseño web" (web design) to the glossary.

How ABC Translations Handles MT Customization and MT Training

Our ABC Go! MT Portal takes the friction out of setting up customization, and it lets that customization run across several MT engines at once. Pull together your glossaries and DNT lists, upload the terms, and they apply to every engine you use. The result is freedom from engine lock-in, so you can switch engines whenever it gets you better results.

On top of that, our MT technology pairs easily with hands-on help from our MT experts. Bring them in, and they will help your company find the MT strategy that fits and figure out the best way to run it.

Whether you are taking your first look at MT, working to strengthen what you already run through customization, or sizing up training as content demands climb, we can help you find the path that fits.

A Side-by-Side Look at MT Training and MT Customization

See Table 2 for a quick comparison of training and customization that helps you settle on the method best suited to your content.

• Definition and Function MT Customization: tuning an engine you already have with glossaries and Do Not Translate (DNT) lists to sharpen the accuracy of machine output. MT Training: building and teaching an engine with broad bilingual data from corpora and Translation Memories (TMs) to raise the accuracy of machine output.
• Purpose MT Customization: lifting MT suggestions for sharper accuracy and a lighter post-editing load. MT Training: strengthening MT suggestions for better accuracy and reduced post-editing.
• Specific Advantages MT Customization: lets companies hold brand names and terminology and handle regional variations. MT Training: lets companies reach a distinct brand voice, tone, style, and regional variations.
• Potential Risks MT Customization: poor execution can yield weak suggestions and pull down overall quality. MT Training: thin training data or too much terminology from inexperienced authors can produce poor suggestions and a drop in overall quality.
• Appropriate Usage MT Customization: best for technical, detail-heavy content that needs exact terminology, or for regional variations when training data is too thin. MT Training: best for highly specialized, marketing, and creative content that needs a specific brand voice, tone, style, or regional variations, with enough data on hand to train.
• Key Success Factors MT Customization: a skilled MT expert who can handle input and output normalization rules, glossaries, and DNT. MT Training: at least 15.6K unique segments to train the engine well.
• Cost Considerations MT Customization: a one-time charge to update the engine profile plus ongoing glossary upkeep; usually friendlier on the budget than training once you weigh the gains. MT Training: an upfront training cost and possible follow-on training expenses over time tied to performance monitoring; worth the spend in the right cases, given the potential payoff.

Table 2. MT Customization vs. MT Training: Key Aspects and Considerations