Post-editing Productivity with Neural Machine Translation: An Empirical Assessment of Speed and Quality in the Banking and Finance Domain
This addresses productivity challenges for professional translators in the banking and finance domain, though it is incremental as it builds on existing NMT research.
The study investigated the impact of neural machine translation (NMT) on post-editing productivity in financial translation, finding that NMT enables substantial time savings and maintains or slightly improves quality, even with under-researched language pairs and limited in-domain data.
Neural machine translation (NMT) has set new quality standards in automatic translation, yet its effect on post-editing productivity is still pending thorough investigation. We empirically test how the inclusion of NMT, in addition to domain-specific translation memories and termbases, impacts speed and quality in professional translation of financial texts. We find that even with language pairs that have received little attention in research settings and small amounts of in-domain data for system adaptation, NMT post-editing allows for substantial time savings and leads to equal or slightly better quality.