Artificial intelligence (AI) is quickly becoming an exciting new technology for the translation industry in form of large language models (LLMs). AI-based functionality could be used to improve the output of neural machine translation (NMT). One main issue that impacts MT quality and reliability is incorrect terminology. This is why STAR is making AI-powered terminology control a priority for its translation products because of the significant gains to be made - greatly improving the quality of MT output, reducing post editing (PE) costs and efforts, and thereby boosting overall translation productivity.

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.

WebTerm Connector is a plugin for STAR MT Translate which combines machine translation with validated terminology information. The aim is to provide “understandable” information in the target language using corporate language and terminology.