Beyond Segments: The Critical Role of Context in Modern Translation

As the translation industry evolves with AI technologies, a fundamental shift is underway—from sentence-level processing to comprehensive document-level understanding. This paradigm change promises to revolutionize translation quality and efficiency for professionals willing to embrace it.

The Context Revolution in Translation Technology

For decades, the translation industry has operated on a fundamental assumption: dividing text into bite-sized segments (typically sentences) is the most efficient way to process content. This segmentation approach has dominated Computer-Assisted Translation (CAT) tools since the earliest days of IBM Translation Manager 2 and Trados, shaping how translators interact with text for nearly 40 years.

But as we witness the rapid evolution of AI-powered translation technologies, it’s becoming increasingly clear that this foundational assumption may be holding us back from achieving truly superior translation quality.

Three Critical Factors for High-Quality Machine Translation

When evaluating what makes machine translation truly useful and high-quality, three factors stand out as paramount:

1. Context scope: How much of the surrounding text is considered during translation
2. Terminology management: How specialized terms are handled and maintained
3. Instructions/prompting: How the system is guided to produce appropriate output

Of these three, context scope may be the most transformative factor that differentiates today’s translation technologies from one another.

The Historical Evolution of Context in Machine Translation

The evolution of machine translation systems can be viewed through the lens of expanding context windows:

• Statistical Machine Translation (SMT) systems primarily translated at the word or phrase level
• Neural Machine Translation (NMT) systems like DeepL^* and LanguageWeaver, even systems based on LMM models, operate at the sentence level
• Generative AI (GenAI) systems can process and translate entire documents as unified wholes

This progression represents a fundamental shift in approach, with each step providing significantly more context for the translation process. The difference between these approaches can be illustrated with a simple thought experiment.

The 20 Expert Translators Problem

Imagine you have a document with 20 sentences and access to the world’s 20 best translators. You assign each translator just one sentence from the document without revealing the entire text, and then combine their work into a final translation.

What would happen?

• Each individual sentence would be brilliantly translated in isolation
• The sentences would likely fail to form a cohesive document
• Terminology would be inconsistent across sentences
• Stylistic inconsistencies would be jarring to readers

This is precisely the problem with NMT systems that process one sentence at a time. Despite the quality of individual sentence translations, the document as a whole suffers from a lack of fluency and connection between sentences and inconsistency.

While domain-specific NMT models can mitigate these issues somewhat (which explains why NMT often performs well with legal or medical texts that have highly standardized terminology and style), general-purpose NMT still struggles with maintaining document-level cohesion.

Even advanced NMT systems based on LLM employ attention mechanisms in very limited way^*. It allows them to capture the context within a sentence. However, they still miss the broader document context that influences meaning, terminology choices, and stylistic consistency.

The Clear Advantages of Document-Level Translation

Translating entire texts rather than individual sentences provides substantial benefits:

• Enhanced cohesion: Global context analysis prevents terminology mistakes and inconsistencies across the document
• Natural flow: Holistic processing generates more flowing text that reads like original content
• Greater precision: Attention mechanisms can identify key elements within the broader text
• Better handling of ambiguity: Words with multiple meanings are translated according to the specific document context
• Improved idiomatic translations: Metaphors and cultural references are understood within their full context

Meanwhile, sentence-level translation continues to present significant challenges:

• Lack of continuity between sentences
• Loss of meaning by ignoring relationships between text sections
• Difficulties with idioms and metaphors that require broader context
• Terminology errors from selecting the most common meaning rather than the contextually appropriate one

The Entrenched Segmentation Paradigm

This sentence-by-sentence approach isn’t limited to NMT systems. The entire translation industry infrastructure is built around segmentation. Virtually all CAT tools follow this approach:

1. Source documents are converted to intermediate files (like XLIFF)
2. Text is divided into sentences or segments
3. The system searches translation memories for identical or similar segments
4. Matching translations are applied
5. Translators work on segments sequentially

For decades, this approach increased translation efficiency, allowing for segment reuse and ensuring consistency across documents or client projects.

Time for a Paradigm Shift?

Moving from segment-level translation to document-level translation requires a global change, starting with translators’ attitudes and ending with a change in the technology stack. Are translators ready to abandon a segmentation philosophy, their  translation memories, searching for hundreds and fuzzy matches, and obsessive attention to consistency issues? Is this segmentation-based approach still optimal today? Two questions challenge the status quo:

• Do identical source sentences truly need identical translations across different documents? While key terminology should remain consistent, the exact translation doesn’t necessarily need to be identical. End users rarely compare translations side-by-side and typically only see one language version.
• Is fixing fuzzy matches from translation memory truly more efficient than post-editing good machine translation? Evidence increasingly suggests that post-editing quality machine translation is both faster and less cognitively demanding for translators.

Practical Steps Toward a Context-Rich Future

The translation industry needs to begin transitioning from segmentation-based approaches to holistic text processing.

New tools and systems optimised for whole documents will not appear overnight. However, with the tools we have today, we can begin the journey towards a document-oriented approach.

While some applications (like software localization) may still require segment-level work, even here, translating all resources for a dialog box together would yield better results than translating individual controls in isolation.

You can start to reap the benefits of the wider context without making revolutionary changes. Practical improvements to our current technology stack without making revolutionary changes could include:

1. Shifting from sentence-level to paragraph-level segmentation
2. Sending entire paragraphs to NMT tools instead of individual sentences
3. Developing NMT tools capable of translating complete paragraphs without internal segmentation^*
4. Treating bulleted/numbered lists and enumerations together with their introductory sentences as single paragraphs

The NMT Technology Gap

Will NMT systems such as DeepL or LanguageWeaver be able to find their place in a document-oriented approach?

At the moment, these systems do not focus their attention on the entire text or larger sections of it.

DeepL does offer the ability to disable sentence splitting (via the split_sentence parameter in their API), but this only works for paragraphs when the submitted text contains no line breaks. When sending text divided into lines, line breaks are removed from the translation in these cases.

A significant competitive advantage awaits whichever translation platform—be it DeepL, LanguageWeaver, or another player—develops the capability to translate longer text fragments containing multiple sentences and line breaks as coherent wholes. Otherwise, once translators have mastered prompting and learned how to link GenAI systems to their CAT software, they will switch to GenAI systems.

How can translators obtain consistent machine translations that take the context of the entire document into account? They can, of course, use GenAI systems. However, there is a problem with this approach: while it is relatively easy to export the source text from CAT tools, it is much more difficult to import text translated by GenAI into a CAT tool. This is where my TransAIde plug-in comes in handy.

About TransAIde project

Before entirely new ecosystems emerge to enable seamless use of GenAI systems in CAT tools, translators, especially individual freelancers and small teams, can start benefiting from the contextual revolution today and take advantage of the exciting opportunities with TransAIde. The TransAIde plug-in for Trados Studio allows the export of larger text chunks to GenAI translation systems that can process entire documents coherently. This helps translators capture the quality benefits of contextual translation while working within familiar CAT environments. I believe, the shift from segment-oriented to document-oriented translation represents one of the most significant paradigm changes in translation technology since the introduction of translation memory. Translators who understand and embrace this transition will be positioned to deliver superior quality while working more efficiently—a rare win-win in an industry constantly balancing quality and productivity demands.

The release of TransAIde is planned for October 2025. Along with the plugin, training materials will be published on how to translate effectively using GenAI and TransAIde systems. The plugin will be available for Trados Studio 2021, 2022, and 2024.

👨‍💻 Dariusz Adamczak, 23 October 2025, dareka@posteditacat.xyz