The availability of language and translation data sets in highly-demanded and business-oriented language pairs as well as in smaller and usually less-resourced languages, be it general or domain/origin-specific data, is crucial for the language and localization industry. The state-of-the-art translation technology is data-driven and learns from the data.
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Nowadays, in one way or another, machine translation (MT) is part of our everyday lives. Most likely Google made that happen, about a decade ago, by launching Google Translate, a free instant online general-purpose translator allowing users to translate any text (words, phrases, documents, web pages) in different language directions.
In part I, we defined the pivot language approach, discussed briefly its major drawbacks, referred to factors regarding the selection of the pivot language and explored two areas where pivoting can be deployed i.e. the relay interpretation (oral) and the human translation (written), including translations from audio recordings with or without script. In part II of this blog article, we will discuss more areas where pivot languages can be deployed, namely in building and enhancing bilingual lexicons, translation memories, machine translation systems and machine transliteration systems.
A pivot language is a third or intermediate language that can bridge the gap between language pairs. For example, if there are translations between English to French and the same English to Spanish available, through the pivot language English, translations between French and Spanish can be generated.