# Incremental Adaptation of NMT for Professional Post-editors: A User Study

arXiv: https://arxiv.org/abs/1906.08996 · HTML: https://mlsanity.com/paper/1906.08996/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Non-Parametric Online Learning from Human Feedback for Neural Machine Translation](https://mlsanity.com/paper/2109.11136/) (2021)
- [Robust Domain Adaptation for Pre-trained Multilingual Neural Machine Translation Models](https://mlsanity.com/paper/2210.14979/) (2022)
- [Beyond Accuracy: Community Perspectives on Machine Translation](https://mlsanity.com/paper/2606.09655/) (2026)

## Abstract (arXiv)

A common use of machine translation in the industry is providing initial translation hypotheses, which are later supervised and post-edited by a human expert. During this revision process, new bilingual data are continuously generated. Machine translation systems can benefit from these new data, incrementally updating the underlying models under an online learning paradigm. We conducted a user study on this scenario, for a neural machine translation system. The experimentation was carried out by professional translators, with a vast experience in machine translation post-editing. The results showed a reduction in the required amount of human effort needed when post-editing the outputs of the system, improvements in the translation quality and a positive perception of the adaptive system by the users.