# MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset

arXiv: https://arxiv.org/abs/2010.04480 · HTML: https://mlsanity.com/paper/2010.04480/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2020.

## Built on by (most cited first, 30 of 47)

- [xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection](https://mlsanity.com/paper/2310.10482/) (2023)
- [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://mlsanity.com/paper/2209.06243/) (2022)
- [Scaling up COMETKIWI: Unbabel-IST 2023 Submission for the Quality Estimation Shared Task](https://mlsanity.com/paper/2309.11925/) (2023)
- [The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Results](https://mlsanity.com/paper/2110.04392/) (2021)
- [Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors](https://mlsanity.com/paper/2110.04399/) (2021)
- [AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages](https://mlsanity.com/paper/2311.09828/) (2023)
- [Translation Error Detection as Rationale Extraction](https://mlsanity.com/paper/2108.12197/) (2021)
- [USCORE: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation](https://mlsanity.com/paper/2202.10062/) (2022)
- [Towards Explainable Evaluation Metrics for Natural Language Generation](https://mlsanity.com/paper/2203.11131/) (2022)
- [Extrinsic Evaluation of Machine Translation Metrics](https://mlsanity.com/paper/2212.10297/) (2022)
- [Optimal Transport for Unsupervised Hallucination Detection in Neural Machine Translation](https://mlsanity.com/paper/2212.09631/) (2022)
- [The Zeno's Paradox of `Low-Resource' Languages](https://mlsanity.com/paper/2410.20817/) (2024)
- [DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages](https://mlsanity.com/paper/2205.12215/) (2022)
- [From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation](https://mlsanity.com/paper/2403.14118/) (2024)
- [Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics](https://mlsanity.com/paper/2410.05183/) (2024)
- [Reproducibility Issues for BERT-based Evaluation Metrics](https://mlsanity.com/paper/2204.00004/) (2022)
- [PreQuEL: Quality Estimation of Machine Translation Outputs in Advance](https://mlsanity.com/paper/2205.09178/) (2022)
- [Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation](https://mlsanity.com/paper/2503.06594/) (2025)
- [Regressive Ensemble for Machine Translation Quality Evaluation](https://mlsanity.com/paper/2109.07242/) (2021)
- [Perturbation-based QE: An Explainable, Unsupervised Word-level Quality Estimation Method for Blackbox Machine Translation](https://mlsanity.com/paper/2305.07457/) (2023)
- [Estimating Machine Translation Difficulty](https://mlsanity.com/paper/2508.10175/) (2025)
- [Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation](https://mlsanity.com/paper/2109.07141/) (2021)
- [Reducing Hallucinations in Neural Machine Translation with Feature Attribution](https://mlsanity.com/paper/2211.09878/) (2022)
- [Is Context Helpful for Chat Translation Evaluation?](https://mlsanity.com/paper/2403.08314/) (2024)
- [Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors](https://mlsanity.com/paper/2302.08975/) (2023)
- [Tailoring Domain Adaptation for Machine Translation Quality Estimation](https://mlsanity.com/paper/2304.08891/) (2023)
- [Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement](https://mlsanity.com/paper/2505.23183/) (2025)
- [X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across Paragraphs](https://mlsanity.com/paper/2309.08873/) (2023)
- [CAPO: Confidence Aware Preference Optimization Learning for Multilingual Preferences](https://mlsanity.com/paper/2511.07691/) (2025)
- [Exploring the Effects of Alignment on Numerical Bias in Large Language Models](https://mlsanity.com/paper/2601.16444/) (2026)

## Abstract (arXiv)

We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE). The dataset contains eleven language pairs, with human labels for up to 10,000 translations per language pair in the following formats: sentence-level direct assessments and post-editing effort, and word-level good/bad labels. It also contains the post-edited sentences, as well as titles of the articles where the sentences were extracted from, and the neural MT models used to translate the text.