How domain‑specific Neural Machine Translation solutions support greater output accuracy and cost efficiencies.

Developing domain-specific NMT engines is a unique undertaking that poses several distinct challenges.

For one, the NMT team requires a much larger pool of data and content to build out the terminology and segments. Additionally, to prepare the NMT engines for the basic production domains, ULG had to ensure consistency in the cleaned and aligned data, train the NMT engines to provide higher output quality and then test the integration quality in a standalone and production workflow.

ULG created custom domain-specific NMT engines that increased the quality scores of the translation output.

Translation quality is scored using multiple evaluation methods, including the standard BLEU quality score which is a computer-generated metric that analyzes the results by comparing the NMT file to a reference file. ULG also uses TER (automated edit distance scoring) and independent Distance Scoring performed by a team of linguists to determine and validate quality scoring of NMT.

When creating the new engines, ULG’s NMT team followed the standard engine customization process and obtained significant BLEU score increases compared to the score of the in-market engine and the scores of the new domain engines.