The continuing growth in digital content means that there is now significantly more linguistic content to translate using more diverse workflows and tools than ever before. This growth necessitates broader requirements for Translation Quality Assessment (TQA) that include appropriate methods for the domain, text type, workflow, and end-user. With this in mind, this volume sheds light on TQA research and practice from academic, institutional, and industry settings in its unique combination of human and machine translation evaluation (MTE). The focus in this book is on the product, rather than the process, of translation. The contributions trace the convergence of post-hoc TQA methods, with cross-pollination from one translation method to another: New error typologies are being taken on for MTE; the concept of ‘fitness for purpose’ when raw or post-edited MT is considered ‘good enough’ is now also used for crowdsourced translation. The state-of-the-art evinces a pragmatic focus, calibrated to a targeted end-user group. Understanding translation technologies and the appropriate evaluation techniques is critical to the successful integration of these technologies in the language services industry of today, where the lines between human and machine have become increasingly blurred and adaptability to change has become a key asset that can ultimately mean success or failure in a competitive landscape.
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