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Customized Machine Translation Mis-Use as a Cause of Shadow (Unpaid)
Translation Work
In “Emerging Technologies As A Cause of Shadow Work”, I have reviewed how emerging
technologies have a tendency to increase the amount of shadow (unpaid) work. In this post, I
review, in more detail, one of the main causes of shadow work in the Language Services
industry.
Over the last five years, enterprises and large language service providers have started to
develop custom Machine Translation engines in an effort to improve translation productivity.
An enterprise, may find, for example, in its translation memories, 50 million words of
translated, product documentation data and use it to build a custom MT engine. When tested
on product documentation, for some language pairs, the custom engine outputs are better than
any other generic machine translation engine (labeled as MT 2 and MT 3 in the lead figure). In
production tests, the custom engine is shown to also lead to increases in translation
productivity so the engine becomes part of the translation production process. Professional
translators are typically paid less to post-edit outputs produced by this engine, but since they
are able to translate more text, overall, their translation income remains the same. The amount
of shadow work they typically do in this instance is small.
Encouraged by their initial success, some enterprises and language service providers become
over enthusiastic: having “proven” that their custom MT engine works well on product
documentation, they go on and deploy it in translation workflows that involve other content
types – customer support and marketing documents, for example. The problem is that custom
MT engines are pretty dumb: although they may produce good outputs on the narrow domains
they have been trained on, they perform abysmally on texts coming from other domains and
content types. In fact, in many instances, outside the domain/content types they have been
trained on, they perform worse than generic, online Machine Translation engines that are
trained on billions of words of human translated data. This state of affairs, which is depicted
graphically on the right side of the lead figure, creates large amounts of shadow (unpaid) work.
Professional translators should be aware of these shortcomings, assess the degree to which the
post-editing work they are asked to do is compensated fairly, and push back on the language
service buyers that mis-use machine translation technology. Post-editing pricing agreed upon in
the context of one domain/content type should never be used in the context of a different
domain/content type.