# Tailoring Domain Adaptation for Machine Translation Quality Estimation

arXiv: https://arxiv.org/abs/2304.08891 · HTML: https://mlsanity.com/paper/2304.08891/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 18 Apr 2023.

## Builds on (most cited first, 19 of 19)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Revisiting Few-sample BERT Fine-tuning](https://mlsanity.com/paper/2006.05987/) (2020)
- [Unsupervised Quality Estimation for Neural Machine Translation](https://mlsanity.com/paper/2005.10608/) (2020)
- [Choosing Transfer Languages for Cross-Lingual Learning](https://mlsanity.com/paper/1905.12688/) (2019)
- [Getting Gender Right in Neural Machine Translation](https://mlsanity.com/paper/1909.05088/) (2019)
- [A Survey of Domain Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1806.00258/) (2018)
- [TransQuest: Translation Quality Estimation with Cross-lingual Transformers](https://mlsanity.com/paper/2011.01536/) (2020)
- [MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset](https://mlsanity.com/paper/2010.04480/) (2020)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing](https://mlsanity.com/paper/1803.07274/) (2018)
- [Poor Man's Quality Estimation: Predicting Reference-Based MT Metrics Without the Reference](https://mlsanity.com/paper/2301.09008/) (2023)
- [Multilingual Multi-Domain Adaptation Approaches for Neural Machine Translation](https://mlsanity.com/paper/1906.07978/) (2019)
- [Better Quality Estimation for Low Resource Corpus Mining](https://mlsanity.com/paper/2203.08259/) (2022)
- [Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts](https://mlsanity.com/paper/2112.06096/) (2021)
- [Original or Translated? On the Use of Parallel Data for Translation Quality Estimation](https://mlsanity.com/paper/2212.10257/) (2022)
- [A New Tool for Efficiently Generating Quality Estimation Datasets](https://mlsanity.com/paper/2111.00767/) (2021)

## Built on by (most cited first, 4 of 4)

- [Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains](https://mlsanity.com/paper/2402.18747/) (2024)
- [Looking under the Wrong Lamppost: On the Limitations of Automated Translation Quality Estimation](https://mlsanity.com/paper/2608.03577/) (2026)
- [Guiding In-Context Learning of LLMs through Quality Estimation for Machine Translation](https://mlsanity.com/paper/2406.07970/) (2024)
- [LocQE: Principled Domain Adaptation for Localisation Quality Estimation by Leveraging Post-Edits](https://mlsanity.com/paper/2609.18720/) (2026)

## Abstract (arXiv)

While quality estimation (QE) can play an important role in the translation process, its effectiveness relies on the availability and quality of training data. For QE in particular, high-quality labeled data is often lacking due to the high cost and effort associated with labeling such data. Aside from the data scarcity challenge, QE models should also be generalizable, i.e., they should be able to handle data from different domains, both generic and specific. To alleviate these two main issues -- data scarcity and domain mismatch -- this paper combines domain adaptation and data augmentation within a robust QE system. Our method first trains a generic QE model and then fine-tunes it on a specific domain while retaining generic knowledge. Our results show a significant improvement for all the language pairs investigated, better cross-lingual inference, and a superior performance in zero-shot learning scenarios as compared to state-of-the-art baselines.