5 Best Practices for Quality Assessment of Machine Translation Output
As we have seen from several posts recently, understanding the quality of your MT output at different points in the process is a key to success with the technology. The more precise your assessment and ability to measure this, the more effective your use experience. The range of quality assessment methodologies can vary from the laborious and expensive TAUS DQF to the minimalist and easy-to-do-wrong BLEU scores. They all have a place once you know what you are doing. MT practitioners must find rapid but accurate ways to assess the quality of the MT output they are creating. This assessment determines all of the following: How much effort is needed to get the output to a defined level of acceptability? How much to pay post-editors given the very specific output an engine produces? How much additional work will be needed to deploy an engine in a production environment? What level of certainty do we have in meeting delivery deadlines given the current quality levels...