We present ACATMT, a compact bilingual encoder-decoder NMT system for English and Swedish, designed for professional computer-assisted translation (CAT) tools. It runs on-device in ONNX format, under 1 GB of RAM with no GPU needed, and features real-time post-edit based terminology adaptation. It also supports translation memory conditioning via decoder prefilling. Evaluation on 5,021 technical segments unseen during training shows significant improvements in COMET and BLEU when using glossaries.

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.