During the TAUS Annual Conference 2016 in October, Eric Bailey, Group Engineering Manager for the Global Service and Experiences team within Office at Microsoft, will host the session 'To Share Or Not To Share'? This blog post is written in preparation for this session.
Recent blog posts
Data entered the field of machine translation in the late eighties and early nineties when researchers at IBM’s Thomas J. Watson Research Center reported successes with their statistical approach to machine translation.
Until that time machine translation worked more or less the same way as human translators with grammars, dictionaries and transfer rules as the main tools. The syntactic and rule-based Machine Translation (MT) engines appealed much more to the imagination of linguistically trained translators, while the new pure data-driven MT engines with probabilistic models turned translation technology more into an alien threat for many translators. Not only because the quality of the output improved as more data were fed into the engines, but also because they could not reproduce or even conceive what really happened inside these machines.
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.