April 2009
By Uwe Muegge

Uwe Muegge is the Director of MedL10N, the life science division of CSOFT. He is currently a member of TC37 at the International Organization for Standardization (ISO) and teaches graduate courses in Terminology Management and Computer-Assisted Translation at the Monterey Institute of International Studies.



Examples of organizations that have created a controlled language





Alcatel: Controlled English Grammar (COGRAM)
Avaya: Avaya Controlled English (ACE)
Caterpillar: Caterpillar Technical English (CTE), Caterpillar Fundamental English (CFE)
Dassault Aerospace: Français Rationalisé
Ericsson: Ericsson English
General Motors (GM): Controlled Automotive Service Language (CASL)
IBM: Easy English
Kodak: International Service Language
Nortel: Nortel Standard English (NSE)
Océ: Controlled English
Siemens: Siemens DokumentationsDeutsch
Scania: Scania Swedish
Sun Microsystems: Sun Controlled English
Xerox: Xerox Multilingual Customized English

Controlled language – does my company need it?

Controlled languages use basis writing rules to simplify sentence structure. Here is how they work and how your company can benefit from introducing a controlled language.

What is controlled language?

A controlled language is a natural language, as opposed to an artificial or constructed language. Natural languages such as English or German are languages that are used by humans for general communication. A controlled language differs from the general language in two significant ways:

  1. The grammar rules of a controlled language are typically more restrictive than those of the general language.
  2. The vocabulary of a controlled language typically contains only a fraction of the words that are permissible in the general language.

As a result, authors who use a controlled language have fewer choices available when writing a text. For example, the sentence “Check the spelling of a paper before publishing it” is a perfectly acceptable sentence in general English. Using CLOUT™, a controlled language rule set developed by the author of this article, the sample sentence would have to be rewritten as “You must check the spelling of your document before you publish that document” to comply with rules regarding vocabulary, active voice, use of articles, and avoidance of pronouns.

Why do we need controlled languages?

Facilitating language learning
When C.K. Ogden developed Basic English in 1930 – probably the first controlled language – the developer had the explicit goal of dramatically reducing the 5+ years it takes to master Standard English. Based on a vocabulary that contains 850 essential words (for comparison, the Oxford English Dictionary defines more than 600.000 words), Basic English is designed to be acquired in a few weeks only.

Eliminating translation
One of the most widely used controlled languages today is ASD-STE100 Simplified Technical English, also known as Simplified English. Simplified English was originally developed by the European Association of Aerospace Manufacturers (AECMA) in the 1980s. The main purpose of Simplified English was to create a variant of Standard English that aircraft engineers with only a limited command of English could understand, thereby eliminating the need to translate maintenance manuals into foreign languages.

Streamlining translation
Within the localization industry, many people familiar with the controlled language concept associate it with automating the translation process. In fact, it typically comes as a surprise that controlled languages can and have been used for purposes other than making the translation process more efficient. By restricting both vocabulary and style, using a controlled language typically improves match rates in translation memory environments and translation quality in (rules-based) machine translation environments.

Enhancing comprehensibility

Helping authors avoid both semantic and syntactic ambiguity has been recognized as a goal worth pursuing in and by itself, especially in the domain of technical communication. Some organizations are deploying a controlled language for the sole purpose of improving the user experience of a product or service on the domestic market.

Common features

A common characteristic of controlled languages is the fact that very little information about their rule sets and vocabularies is freely available. This is not really surprising considering that a controlled language can provide an organization with a distinct advantage over its competitors.

Apart form that, you will find very few similarities between controlled languages. Nortel Standard English, for instance, has only a little over a dozen rules, while Caterpillar Technical English consists of more than ten times as many. And a recent comparative analysis of eight controlled English languages found that the number of shared features was exactly one, i.e. a preference for short sentences.

Should I use a controlled language?

Here is why it makes sense for your company to use a controlled language:

Improved usability
Documents that are more readable and more comprehensible improve the usability of a product or service and reduce the number of support calls.

Objective metrics and author support
Tools-driven controlled language environments enable the automation of many editing tasks and provide objective quality metrics for the authoring process. Controlled-language environments also provide authors with powerful tools that give them objective and structured support in a typically rather subjective and unstructured environment.

Lower translation costs
As controlled-language texts are more uniform and standardized than uncontrolled ones, controlled-language source documents typically have higher match rates when processed in a translation memory system. Higher match rates mean lower translation cost and higher translation speed.

Some controlled languages have been specifically designed with machine translation in mind, e.g. Caterpillar Technical English or the Controlled Language Optimized for Uniform Translation CLOUT. Using a controlled language customized for a specific machine translation system will significantly improve the quality of machine-generated translation proposals and dramatically reduce the time and cost associated with a human translator or editor.

Impacts on translation

Even in environments that combine content management systems with translation memory technology, the percentage of untranslated segments can still remain fairly high in new projects. This can be a major challenge for organizations that wish to reduce the cost and time involved in the translation of their materials. While it is certainly possible to manage content on the sentence/segment level, the current best practice seems to be to chunk at the topic level. This means that reuse occurs at a fairly high level of granularity. In other words: There is too much variability within these topics!
Controlled authoring for translation memory systems

Writing in a controlled language reduces variability, especially if the controlled language not only covers grammar, style, and vocabulary, but also function.
In a functional approach to controlled language authoring, there are specific rules for text functions such as instruction, result, or warning message. Here are two simple examples for functional controlled language rules:

Text function: Instruction
Pattern: Verb (infinitive) + article + object + punctuation mark.
Example: Click the button.

Text function: Result
Pattern: Article + object + verb (present tense) + punctuation mark.
Example: The window “Expense Report” appears.

Implementing functional controlled language rules will enable authors to write text in which sentences with the same function have a very high degree of similarity. This not only makes sentence modules reusable within and across topics in a content management system, it also dramatically improves match rates in a translation memory system.

Controlled authoring for rules-based machine translation systems

While uniformity is the decisive factor in improving efficiency in a traditional translation memory environment, reducing ambiguity in the source text makes machine translation more productive. The problem that rules-based machine translation systems like Systran struggle with, is the fact that in uncontrolled source texts, the (grammatical) relationship between the words in a sentence is not always clear. To enable rules-based machine translation systems to generate better translations, the controlled language needs to have rules like the following that helps the machine translation system to successfully identify the part of speech of each word in a sentence:

Write sentences that have articles before nouns, where possible.
Do not write: Click button to launch program.
Write: Click the button to launch the program.

Write sentences that repeat the noun instead of writing a pronoun.
Do not write: The button expands into a window when you click it.
Write: The button expands into a window when you click the button.

With rules in place that mitigate the weaknesses of rules-based machine translation systems, the quality of their output is bound to improve dramatically.

Software-driven rules checking
Not at all! Today, many organizations that wish to reap the benefits of controlled-language authoring opt for a software-driven solution that comes with a built-in set of grammar and style rules. Systems like acrolinx IQ Suite, IAI CLAT, or Tedopres HyperSTE have enabled thousands of organizations to improve the quality and productivity of their authoring and translation processes. In a software-driven authoring environment, organizations don’t have to maintain the staff of highly trained linguistic experts needed to develop and deploy a proprietary controlled language. Instead, the organization simply selects the rules that are most suitable for a given content type from a set of preexisting writing rules. Typically, these checking tools support the definition of multiple sets of rules for multiple types of content (e.g. stricter rules for user documentation than for knowledge base articles).

Terminology management support
From a technology standpoint, it is relatively easy to implement the rules part of a controlled language; the terminology part is typically more labor intensive. It is certainly true that many controlled language software solutions include a module for collecting terminology, but still the task of creating a corporate dictionary, which is what this job amounts to, might be a daunting one. Not only will all synonyms among the possibly thousands of terms in use within the organization have to be identified, but these synonyms will also have to be categorized into preferred and deprecated (do not use) terms. While creating a corporate dictionary may be a challenge, once it is available, that dictionary may also be the feature most valued by the users of the controlled language system.

Examples of controlled language

To see an implementation of a simple controlled language designed for machine translation, visit the author’s website at www.muegge.cc. The entire site was written in CLOUT, the Controlled Language Optimized for Machine Translation. On the home page, click on any of the language combinations into English, i.e. German > English or French > English and watch how Systran's free machine translation system turns a complete website into a fully navigable, highly comprehensible virtual English site in real time. Click on the link Controlled Language/Rules for Machine to see ten sample CLOUT writing rules that have a high impact on the comprehensibility and (machine) translatability of instructional text in English.


  • Ogden, Charles Kay. 1930. Basic English: A General Introduction with Rules and Grammar. London : Treber, 1930.
  • Basic English Institute. 1996. Ogden's Basic English Word List. Ogden's Basic English. [Online] [Cited: February 3, 2009.]
  • AeroSpace and Defence Industries Association of Europe. 2005. ASD-STE100 - Simplified Technical English - International Specification for the Preparation of Maintenance Documentation in a Controlled Language. ESSAS Electronic Supporting System for ASD Standardization. [Online] 2005. [Cited: February 3, 2009.] http://www.asd-stan.org/sales/asdocs.asp
  • O'Brien, Sharon. 2003. Controlling Controlled English: An Analysis of Several Controlled Language Rule Sets. Machine Translation Archive. [Online] 2003. [Cited: February 3, 2009.] http://www.mt-archive.info/CLT-2003-Obrien.pdf