Translators come from various walks of life – former computer programmers, long-time linguists, stay-at-home mothers, former government officials and everything in-between. Because of the diverse skill set required by the translation profession, many translators do not actually ever study translation. They grow-up bilingual or learn languages as a hobby, have a particular aptitude with words, and are familiar with a particular subject matter. In-depth knowledge of a particular genre of text is usually paramount among qualifications. So as much as studying translation is a legitimate pursuit in higher education, it is by no means a prerequisite for becoming a legitimate professional translator. Consequently, many of the long researched questions in translation are not widely known among translators. The findings of the majority of this research are often not influential on the day-to-day work of translation. While it is not a surprise to me that the intellectual pursuits of academia usually do not guide the business world, I do find it lamentable in some cases.
One case in particular is that of translationese. Translationese is the term for the characteristics of language that manifest themselves more abundantly in translated text as opposed to a text generated natively in that language. Researchers have long hypothesized which characteristics or features are unique to translated text, both dependent and independent of source or target languages involved. Here are a few of the features hypothesized: text length, sentence complexity, vocabulary, etc. Essentially translated text is usually more explicit, simpler in vocabulary, and normalized to perceived cultural and language tendencies. Language specific characteristics of translated text vary widely.
There were many early studies on the matter that seemed to validate the hypothesis. Recently, more complex tools and algorithms have been applied to the question and validated it beyond question. Modern tools can classify a text as either translated or original with accuracy greater than 90%.
So how should/could this academic finding influence day-to-day translation? Imagine with me a CAT tool that can identify instances of this “translationese.” One that can show a translator, a project manager, a client, areas of translated text that identify the translation as a translation. Imagine a tool that could suggest a revision that might be less “translationese” and more natural. Sound interesting? I think so.
But there isn’t anything like that…yet. Here at Western Standard, we would like to pursue this technology and include it in Fluency, but we want to know if it would be useful for our users/perspective users. How does it rank on your wish list? The speed with which we undertake this endeavor will be commensurate with the interest expressed, so if you think this would be useful, let us know.