Q8 – What about “translator resistance” to become a post-editor?

If you remember the translator’s resistance to use CAT tools in the late 90’s (I do, I freelanced in the UK those days), you will get an idea of how post-editing may be viewed in 2010 and onwards.

Every “new” technology (or technique) always faces resistance. There is nothing we love more than security, certainties. In the translation world, this means the relatively long (?) cycle of learning CAT tools. We do not mean the omnipresent tools which are marketed so well, but also the lesser known tools that can also do the job pretty well. Some have made a conscious effort to offer plug-ins to MT (Swordfish, from maxprograms.com) and, like PangeaMT are designed on open standards with a “no lock-in” mentality. Now you are telling your translators to “correct” machine output and at a lower fee. Back to the 90’s…

Indeed there may be some resistance from long-standing translators. Recent graduates are still trained in translation theory linked to computer-assisted tools.

However, now that end users can in certain contexts play with already built systems, even if not fully customized to their domain, the postediting stage may become a selection criterion. Before full deployment, corporations, organizations, industries and LSPs usually  run quantifiable evaluation pilots to become accustomed to post-editing tasks, identifying recurrent changes for automatic solutions, and base expectations about quality and pricing on objective data. This means that future post-editors, whether they are current translators or new recruits need to be involved at some stage prior to deployment.

Post-editing is still a nascent profession and experimentation with MT systems is required to gain a set of skills relative to each language. For example, if you are running an engine which lacks general “world” vocabulary, or very usual words. This may be annoying in large-scale systems and we run statistical dictionary modules to add words which were not in your training corpus. Nevertheless, post-editors in localization or documentation environments may think it is better to leave unknown terms in the source language so they can run “search & replace” and post-edit quickly. Thus, do expect the same resistance any new technology encounters but explain the benefits of it. Human translation cannot resolve the issues in terms of speed and cost in the digital content era. There are simply not enough qualified translators and even if there were, the logistics and costs of translating 50.000 words in a day or two would make project managers crazy. These pressures may also explain the high human “turn around” in the language industry. The truth is that with the advent of online translation services and desktop services and MT server engines, machines translate more words than humans already….