Technology: A brief history of machine translation

Artificial intelligence is drastically transforming the translation industry. However, it is by no means the first technology to have done so. How professional translation has continually reinvented itself—and why human oversight remains the crucial element in the process. An overview.

They translated or copied line by line with an inkwell and a quill pen—if necessary, even by candlelight and with a magnifying glass. But in any case, they did it with incredible patience. The Franciscan monks described by Umberto Eco in “The Name of the Rose” (and brought to the screen by Jean-Jacques Annaud) had to avoid mistakes at all costs, because correcting them required a great deal of effort.

Umberto Eco’s monks toiled away in the year 1327, but they were part of a centuries-old tradition. Translations were already being produced this way in antiquity. Well into the 20th century, the only changes were that lighting, writing implements, and eyeglasses gradually improved. Two centuries later, the printing press in Europe ensured that dictionaries and encyclopedias began to support the work, but the major advances did not occur until much later.

The 20th Century: Translation remains a manual task for a long time

Even in the 20th century, translation remained an “analog” activity. A slow, highly concentrated process. The source texts were available in printed or handwritten form, and the target texts were produced by hand or on a typewriter. Professional translators used dictionaries, specialized encyclopedias for specific industries, and perhaps even glossaries they compiled themselves in folders or card catalogs. For a single word, translators often had to scroll through several volumes until they found a suitable definition. Research did not involve entering a search term, but rather working through indexes and cross-references, often using sources from libraries as well.

Obviously, manual research extended the project timeline, which in turn increased overall costs. Consistency was primarily based on the translator’s memory and notes. When multiple people worked for the same client, they had to coordinate efficiently to maintain consistent terminology and tone. There was no central database, but rather personal “islands of knowledge” that evolved over time. Translators had their own systems of notes, markings, and mnemonic aids. These limitations made scalability impossible.

The PC revolution: Technology transforms translation

Everything changed with the widespread adoption of personal computers in the early 1990s. But translation software and the internet didn’t exist yet. The revolution had a different name: Word processing.

Applications like Word or its predecessors made it possible to save, copy, move, and edit texts without creating piles of paper. Typos could be corrected without having to rewrite an entire page. Versions could be stored as files instead of in thick folders.

At the same time, the first digital dictionaries and encyclopedias emerged, initially on CD-ROM and later also online. This allowed translators to work significantly faster. Suddenly, a keyword search was enough to find relevant multilingual entries in seconds. The introduction of digital technical encyclopedias and search functions allowed users to combine multiple keywords and find cross-references more quickly.

This period marked the inception of the earliest databases for technical terms, often still on a project- or company-specific basis. They made it possible to save the terms preferred by specific clients. However, the industry had not yet developed what we now call translation memory. Databases typically stored only individual words or short phrases, not entire translation units including context.

The impact on speed and error rates was obvious. Texts could be revised more rapidly, typos were caught by spell-checkers, and electronic reference works made it easier to look up many terms. Nonetheless, the process remained highly linear. The source text was translated from top to bottom, and each phrase was written from scratch as it happened.

CAT Tools and Translation Memory: Translation technology is making enormous strides

The next big step was CAT. Computer-Aided Translation (CAT) made use of a principle that is taken for granted today. Texts were broken down into small units, usually sentences or sentence-like segments. The target language version was saved for each segment. If a similar or identical sentence appeared later, the tool suggested the existing translation.

This collection of segment pairs was called a translation memory. It acted as the long-term memory of a translation agency. Whenever recurring phrases appeared, for example, in product descriptions, user manuals, or help articles, the system made it possible to reference existing translations. For companies, this meant more consistent texts; for translators, less redundancy.

Term databases were an important complement to this new technology. They store preferred technical terms, product names, legally relevant phrasing, and style guidelines. While the translation memory recognized entire sentences or segments, the terminology database ensured that the same technical terms were always used within these sentences.

Machine translation: From rules to neural networks

Parallel to the development of CAT tools, a longstanding vision shifted from concept to reality: machine translation. Early systems operated on a rule-based basis. Linguists and computer scientists formulated grammar and vocabulary rules that the system applied step by step. At first, these approaches were more of a proof of concept, and the results were still quite rough.

Later, statistical systems were developed to analyze large amounts of bilingual text and derive probabilities from it. Fixed rules were thus replaced by statistical patterns. This approach noticeably improved the quality of the translation, but it remained vulnerable to unusual phrasing and peculiar sentence structures.

The biggest leap forward came with neural machine translation. In this process, artificial neural networks were trained to better grasp the connections within the text. The results of machine translation now flowed more fluently, sounded more natural, and often came surprisingly close to human-written drafts. It significantly changed how people perceived it. What was once considered barely usable was now widely regarded as surprisingly good—if not perfect.

Differentiating between application areas

Despite the progress, there was still a wide gap between a raw translation from large language models and a professionally edited text. Raw machine translations were suitable for internal purposes, for quick orientation, or to get a rough idea of the content. However, this level of accuracy was generally insufficient for high-quality communication, legally relevant documents, or texts with a clear brand message. This is where post-editing came into play. Translators edited machine-generated suggestions, corrected errors, adapted style, terminology, and tone, and ensured that the text was impeccably crafted, striking the perfect balance between its structure, message, and cultural context.

Areas of application emerged based on this distinction. For large volumes with a limited budget or for internal communication, machine translation with professional post-editing (MTPE) offered a cost-effective option. But a higher degree of human oversight was required for brand communication, marketing texts, legal content, or specialized medical communication. In this context, the focus was on ensuring the text’s impact and accuracy, making human translation indispensable.

The status quo – a fully stocked toolkit for translators

Modern translation agencies today rely on a combination of various technologies. The typical workflow begins with an analysis of the source text. CAT tools check which portions are already present in existing translation memories, how many repetitions there are, and which parts are entirely new.

This analysis is used to assess the required effort and best approach. Depending on the text type and requirements, machine translation can also be integrated. The system provides suggestions that are displayed directly in the CAT tool. Translators then see the source text, possible suggestions from the translation memory, and a machine-generated preliminary translation into the target language side by side. This view allows them to decide whether to accept a suggestion, adapt it, or rephrase it.

Previous translations and stored terminology thus form a database that gradually improves the quality. Every completed translation adds to the translation memory. Every approved terminology entry improves consistency in future projects. Over time, individual projects build a linguistic profile tailored to each client.

This process is supported by specialized QA tools. They automatically check numbers, date formats, tags, references, and other formal aspects. They can also determine whether all numbers have been correctly transferred, whether formatting is consistent, and whether certain prohibited terms are avoided. The quality of the translation in terms of content and style remains the responsibility of humans, but many potential careless errors can be automatically caught.

The limitations of machines – why humans are irreplaceable

conveys not only information but also tone, cultural allusions, knowledge of the target audience, and emotions. Machines can recognize patterns and calculate probabilities, but they lack an understanding of context in the human sense.

An intuitive understanding of how a text will be received by readers is often what makes the difference, especially when it comes to ambiguous phrasing, irony, humor, or culturally sensitive content. Translators do more than just choose words. They craft a message. They consider industry-specific nuances, the expectations of the target audience, and the role a text plays in a company’s overall communication strategy.

Even brand messages can only be automated to a certain degree. A brand thrives on its tone, recurring imagery, and unique style. These elements require decisions that go beyond statistically obvious choices. Technology can provide support, offer suggestions, and ensure consistency. However, strategic direction and deliberate fine-tuning remain human tasks.

Outlook: Where is translation technology headed?

The development of translation technology will remain dynamic in the coming years. We can expect even better integration of tools. CAT tools, machine translation, terminology management, and project management will converge more closely. Processes that are still initiated manually today will likely be partially automated, including analyzing incoming files and routing them to the appropriate language teams or approval and quality workflows.

At the same time, greater specialization is expected. Systems will be optimized for specific domains, text types, or industries. Data homogeneity maximizes the efficacy of statistical and neural models.

This leads to another key factor: Data quality. Existing translations, well-maintained terminology databases, and consistent style guides form the foundation for effective technical support. Poor or inconsistent data, on the other hand, leads to suggestions that create more work than they save.

The history of translation technologies thus demonstrates one thing above all. Progress thrives at the intersection of human insight and technological innovation. This was true in the scriptorium as much as it is in today’s digital translation agency.

Timeline: An overview of the history of translation technology

▶ Antiquity to ~1327

Handwritten Translation: Monks and scribes copy and translate texts by hand using quill pens and inkwells.

▶ From the 15th century onward

Printing in Europe: Dictionaries and encyclopedias begin to support translation efforts.

▶ 20. 20th Century (through the 1980s)

Translation remains analog: Source texts on paper, target texts by hand or typewriter; research using printed encyclopedias and libraries.

▶ Early 1990s

PC revolution and word processing: Applications like Word enable digital writing, saving, and editing of texts; the first digital dictionaries appear on CD-ROM.

▶ From the mid-1990s

CAT Tools and Translation Memory: Texts are broken down into segments; translations are stored and reused; term databases ensure terminological consistency.

▶ 2000s

Statistical machine translation: Systems analyze large amounts of bilingual text and use this data to calculate translation probabilities.

▶ From ~2016

Neural machine translation (NMT): Artificial neural networks produce more fluent, natural-sounding translations.

▶ Today

Integrated workflows: Machine translation, terminology management, and QA tools work together; post-editing by human translators remains a key quality factor.