# Post-editing Productivity with Neural Machine Translation: An Empirical Assessment of Speed and Quality in the Banking and Finance Domain

arXiv: https://arxiv.org/abs/1906.01685 · HTML: https://mlsanity.com/paper/1906.01685/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [Democratizing Neural Machine Translation with OPUS-MT](https://mlsanity.com/paper/2212.01936/) (2022)
- [Neural Machine Translation Quality and Post-Editing Performance](https://mlsanity.com/paper/2109.05016/) (2021)
- [DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages](https://mlsanity.com/paper/2205.12215/) (2022)
- [Is post-editing really faster than human translation?](https://mlsanity.com/paper/2312.12660/) (2023)
- [Assessing Post-editing Effort in the English-Hindi Direction](https://mlsanity.com/paper/2112.09841/) (2021)
- [Domain Adaptation for Arabic Machine Translation: The Case of Financial Texts](https://mlsanity.com/paper/2309.12863/) (2023)
- [Creating Domain-Specific Translation Memories for Machine Translation Fine-tuning: The TRENCARD Bilingual Cardiology Corpus](https://mlsanity.com/paper/2409.02667/) (2024)

## Abstract (arXiv)

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.