# Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains

arXiv: https://arxiv.org/abs/2402.18747 · HTML: https://mlsanity.com/paper/2402.18747/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 38 references that are in the map.
- First posted on arXiv 28 Feb 2024.

## Builds on (most cited first, 18 of 18)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013)
- [Large Language Models Are State-of-the-Art Evaluators of Translation Quality](https://mlsanity.com/paper/2302.14520/) (2023)
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing](https://mlsanity.com/paper/2004.14564/) (2020)
- [The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation](https://mlsanity.com/paper/2308.07286/) (2023)
- [Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation](https://mlsanity.com/paper/1808.07048/) (2018)
- [Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric Into a Document-Level Metric](https://mlsanity.com/paper/2209.13654/) (2022)
- [BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation](https://mlsanity.com/paper/2210.07626/) (2022)
- [Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity](https://mlsanity.com/paper/2008.04935/) (2020)
- [A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism](https://mlsanity.com/paper/2401.05749/) (2024)
- [Reassessing Claims of Human Parity and Super-Human Performance in Machine Translation at WMT 2019](https://mlsanity.com/paper/2005.05738/) (2020)
- [Poor Man's Quality Estimation: Predicting Reference-Based MT Metrics Without the Reference](https://mlsanity.com/paper/2301.09008/) (2023)
- [Tailoring Domain Adaptation for Machine Translation Quality Estimation](https://mlsanity.com/paper/2304.08891/) (2023)

## Built on by (most cited first, 21 of 21)

- [X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scale](https://mlsanity.com/paper/2410.03115/) (2024)
- [Improving Statistical Significance in Human Evaluation of Automatic Metrics via Soft Pairwise Accuracy](https://mlsanity.com/paper/2409.09598/) (2024)
- [How to Select Datapoints for Efficient Human Evaluation of NLG Models?](https://mlsanity.com/paper/2501.18251/) (2025)
- [AI-Assisted Human Evaluation of Machine Translation](https://mlsanity.com/paper/2406.12419/) (2024)
- [Can Automatic Metrics Assess High-Quality Translations?](https://mlsanity.com/paper/2405.18348/) (2024)
- [Pitfalls and Outlooks in Using COMET](https://mlsanity.com/paper/2408.15366/) (2024)
- [Watching the Watchers: Exposing Gender Disparities in Machine Translation Quality Estimation](https://mlsanity.com/paper/2410.10995/) (2024)
- [Has Machine Translation Evaluation Achieved Human Parity? The Human Reference and the Limits of Progress](https://mlsanity.com/paper/2506.19571/) (2025)
- [Adapters for Altering LLM Vocabularies: What Languages Benefit the Most?](https://mlsanity.com/paper/2410.09644/) (2024)
- [Modeling User Preferences with Automatic Metrics: Creating a High-Quality Preference Dataset for Machine Translation](https://mlsanity.com/paper/2410.07779/) (2024)
- [QE4PE: Word-level Quality Estimation for Human Post-Editing](https://mlsanity.com/paper/2503.03044/) (2025)
- [Are Generative Models Underconfident? Better Quality Estimation with Boosted Model Probability](https://mlsanity.com/paper/2502.11115/) (2025)
- [Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement](https://mlsanity.com/paper/2505.23183/) (2025)
- [COMET-poly: Machine Translation Metric Grounded in Other Candidates](https://mlsanity.com/paper/2508.18549/) (2025)
- [Reward Models are Metrics in a Trench Coat](https://mlsanity.com/paper/2510.03231/) (2025)
- [Who Watches the Watchmen? Humans Disagree With Translation Metrics on Unseen Domains](https://mlsanity.com/paper/2604.17393/) (2026)
- [Creativity Bias: How Machine Evaluation Struggles with Creativity in Literary Translations](https://mlsanity.com/paper/2605.13596/) (2026)
- [Learning to Translate Ambiguous Terminology by Preference Optimization on Post-Edits](https://mlsanity.com/paper/2507.03580/) (2025)
- [Sigmoid Head for Quality Estimation under Language Ambiguity](https://mlsanity.com/paper/2601.00680/) (2026)
- [LocQE: Principled Domain Adaptation for Localisation Quality Estimation by Leveraging Post-Edits](https://mlsanity.com/paper/2609.18720/) (2026)
- [TermJudge: A Document-Level Metric Judging, Not Counting, Terminology in Machine Translation Evaluation](https://mlsanity.com/paper/2609.35017/) (2026)

## Abstract (arXiv)

We introduce a new, extensive multidimensional quality metrics (MQM) annotated dataset covering 11 language pairs in the biomedical domain. We use this dataset to investigate whether machine translation (MT) metrics which are fine-tuned on human-generated MT quality judgements are robust to domain shifts between training and inference. We find that fine-tuned metrics exhibit a substantial performance drop in the unseen domain scenario relative to metrics that rely on the surface form, as well as pre-trained metrics which are not fine-tuned on MT quality judgments.