As machine translation (MT) is being more tightly integrated into modern CAT-based translation workflows, measuring and increasing MT efficiency has become one of the main concerns of LSPs and companies trying to optimise their processes in terms of quality and performance. When it comes to measur-ing MT efficiency, STAR’s CAT tool Transit NXT offers post-editing distance (PED) and MT error categorisation as two core features of Transit’s compre-hensive QA module. With DeepL glossa-ry integration and MT confidence scores, translators will also have access to two new features which can help them in-crease their MT post-editing efficiency.
When interested in an internal web ap-plication for MT, corporate customers always ask how reliable terminology will be in their translations. Coherent vocabulary is crucial in many aspects of corporate translations, such as doc-umentation or marketing. The main goal every MT provider would like to achieve is to fully integrate the cus-tomer’s terminology into the model, so that the result does not need to be edit-ed, but this is still not always guaran-teed. Besides, a web application like STAR MT Translate allows our cus-tomers to use – integrated within the same page – different generic MT pro-viders which were not trained with customer-specific data. So, as a prag-matic approach, we decided to in-crease the level of integration between WebTerm and STAR MT Translate, adding to the latter more terminological information, with which the user can post-edit the translation if needed.
Large quantities of multilingual legal documents are waiting to be regularly aligned and used for future translations. For reasons of time, effort and cost, manual alignment is not an option. Automatically aligned segments are suitable for concordance search but are unreliable for fuzzy search and pretranslation. MT-based alignment could be the key to improving the results.
After many successful experiments it has become evident that smart pre- and post-processing can significantly improve the output of neural machine translation. Therefore, various generic and language-specific processes are applied to the training corpus, the user input and the MT output for STAR MT Translate.