# Domain specialization: a post-training domain adaptation for Neural Machine Translation

arXiv: https://arxiv.org/abs/1612.06141 · HTML: https://mlsanity.com/paper/1612.06141/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2016.

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

- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [Robust Domain Adaptation for Pre-trained Multilingual Neural Machine Translation Models](https://mlsanity.com/paper/2210.14979/) (2022)
- [Soft Alignment Objectives for Robust Adaptation of Language Generation](https://mlsanity.com/paper/2211.16550/) (2022)

## Abstract (arXiv)

Domain adaptation is a key feature in Machine Translation. It generally encompasses terminology, domain and style adaptation, especially for human post-editing workflows in Computer Assisted Translation (CAT). With Neural Machine Translation (NMT), we introduce a new notion of domain adaptation that we call "specialization" and which is showing promising results both in the learning speed and in adaptation accuracy. In this paper, we propose to explore this approach under several perspectives.