Best Practices on how to use machine translation in a translation workflow
Collecting "Best Practices" for working with machine translation in existing (or not yet existing) environments should help developers to adapt their technology to match the expectation of translators.
Here is one: Have several MT engines connected within your translation environment simultaneously, don't look at the MT suggestions as a whole and only use them if they match your typing through AutoSuggest/AutoWrite etc. The benefit is that you are not unduly influenced with sub-par suggestions (which can be a big problem), you speed up the translation process because the system completes "your thoughts" and you drive the process vs. the machine driving you.
Challenges in teaching language technologies
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Is technology distracting translators from their core work?
Observing in-house translators and proofreaders, I have had the feeling that something had changed since the time I used to translate myself. I was wondering what it was until I noticed that they were using a fanstactic and powerful spellcheck tool. That was great but at the same time, they overlooked so many typos or errors that I got confused. When I made them notice these mistakes, they all answered, "don't worry the tool will correct the mistakes". So I am wondering:
Is it possible to proofread a text overlooking part of the mistakes? How can the brain efficiently segment mistakes to notice vs mistakes to overlook? Can a text be seen as layers instead of a whole?
Sub-topic initially created by Marie-Sophie Petit.
Tools and Creativity: a possible match?
When you translate for a niche or highly creative, marketing texts, are the tools a limit to your imagination? Are there some tools created with this approach in mind: keeping high pace productivity while being able to twist the words at will?
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