It is already clear that at 2,000-3,000 words per day per translator that demand is many multiples of supply. LSPs are having trouble finding qualified and skilled translators – In part due to lower rates in the market and more competition for resources Wave of new LSPs and translators – Many will try to capitalize on the market opportunity created by translator shortage, but with deliver sub-standard services – Lack of experience – both new LSPs and translators – Lower quality translations will become more common place
28,000 0 3,000 6,000 9,000 12,000 25,000 21,000 18,000 15,000 Human Translation Fastest MT + Post Editing *Fastest MT + Post Editing Speed reported by clients. * Words Per Day Per Translator Average person reads 200-250 words per minute. 96,000-120,000 in 8 hours. ~35 times faster than human translation.
Test Set Data should be very high quality: – If the test set data are of low quality, then the metric delivered cannot be relied upon. – Proof read a test set. Don’t just trust existing translation memory segments. Test set should be in domain: – The test set should represent the type of information that you are going to translate. The domain, writing style and vocabulary should be representative of what you intend to translate. Testing on out-of-domain text will not result in a useful metric. Test Set Data must not be included in the training Data: – If you are creating an SMT engine, then you must make sure that the data you are testing with or very similar data are not in the data that the engine was trained with. If the test data are in the training data the scores will be artificially high and will not represent the same level of quality that will be output when other data are translated. The criteria specified by this checklist are absolute. Not complying with any of the checklist items will result in a score that is unreliable and less meaningful.
4. Manage Manage translation projects while generating corrective data for quality improvement. 2. Measure Measure the quality of the engine for rating and future improvement comparisons 3. Improve Provide corrective feedback removing potential for translation errors. 1. Customize Create a new custom engine using foundation data and your own language assets