# There's no Data Like Better Data: Using QE Metrics for MT Data Filtering

arXiv: https://arxiv.org/abs/2311.05350 · HTML: https://mlsanity.com/paper/2311.05350/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 60 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 13 of 13)

- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [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)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation](https://mlsanity.com/paper/2008.03703/) (2020)
- [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://mlsanity.com/paper/2209.06243/) (2022)
- [Unsupervised Domain Clusters in Pretrained Language Models](https://mlsanity.com/paper/2004.02105/) (2020)
- [On the Impact of Various Types of Noise on Neural Machine Translation](https://mlsanity.com/paper/1805.12282/) (2018)
- [Learning Joint Multilingual Sentence Representations with Neural Machine Translation](https://mlsanity.com/paper/1704.04154/) (2017)
- [OpenKiwi: An Open Source Framework for Quality Estimation](https://mlsanity.com/paper/1902.08646/) (2019)
- [Dual Conditional Cross-Entropy Filtering of Noisy Parallel Corpora](https://mlsanity.com/paper/1809.00197/) (2018)
- [Low-Resource Corpus Filtering using Multilingual Sentence Embeddings](https://mlsanity.com/paper/1906.08885/) (2019)
- [Microsoft's Submission to the WMT2018 News Translation Task: How I Learned to Stop Worrying and Love the Data](https://mlsanity.com/paper/1809.00196/) (2018)

## Built on by (most cited first, 19 of 19)

- [Navigating the Metrics Maze: Reconciling Score Magnitudes and Accuracies](https://mlsanity.com/paper/2401.06760/) (2024)
- [A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism](https://mlsanity.com/paper/2401.05749/) (2024)
- [Enhancing Multilingual LLM Pretraining with Model-Based Data Selection](https://mlsanity.com/paper/2502.10361/) (2025)
- [Granary: Speech Recognition and Translation Dataset in 25 European Languages](https://mlsanity.com/paper/2505.13404/) (2025)
- [Introducing the NewsPaLM MBR and QE Dataset: LLM-Generated High-Quality Parallel Data Outperforms Traditional Web-Crawled Data](https://mlsanity.com/paper/2408.06537/) (2024)
- [Pitfalls and Outlooks in Using COMET](https://mlsanity.com/paper/2408.15366/) (2024)
- [Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics](https://mlsanity.com/paper/2410.05183/) (2024)
- [Watching the Watchers: Exposing Gender Disparities in Machine Translation Quality Estimation](https://mlsanity.com/paper/2410.10995/) (2024)
- [Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question Answering](https://mlsanity.com/paper/2504.07583/) (2025)
- [xCOMET-lite: Bridging the Gap Between Efficiency and Quality in Learned MT Evaluation Metrics](https://mlsanity.com/paper/2406.14553/) (2024)
- [Recovering document annotations for sentence-level bitext](https://mlsanity.com/paper/2406.03869/) (2024)
- [Adding Chocolate to Mint: Mitigating Metric Interference in Machine Translation](https://mlsanity.com/paper/2503.08327/) (2025)
- [Cross-lingual Human-Preference Alignment for Neural Machine Translation with Direct Quality Optimization](https://mlsanity.com/paper/2409.17673/) (2024)
- [How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise in Machine Translation](https://mlsanity.com/paper/2407.02208/) (2024)
- [PromptOptMe: Error-Aware Prompt Compression for LLM-based MT Evaluation Metrics](https://mlsanity.com/paper/2412.16120/) (2024)
- [Are Generative Models Underconfident? Better Quality Estimation with Boosted Model Probability](https://mlsanity.com/paper/2502.11115/) (2025)
- [Hacking Neural Evaluation Metrics with Single Hub Text](https://mlsanity.com/paper/2512.16323/) (2025)
- [FairQE: Multi-Agent Framework for Mitigating Gender Bias in Translation Quality Estimation](https://mlsanity.com/paper/2604.21420/) (2026)
- [Model-Based Quality Assessment for Massively Multilingual Parallel Data](https://mlsanity.com/paper/2606.00285/) (2026)

## Abstract (arXiv)

Quality Estimation (QE), the evaluation of machine translation output without the need of explicit references, has seen big improvements in the last years with the use of neural metrics. In this paper we analyze the viability of using QE metrics for filtering out bad quality sentence pairs in the training data of neural machine translation systems~(NMT). While most corpus filtering methods are focused on detecting noisy examples in collections of texts, usually huge amounts of web crawled data, QE models are trained to discriminate more fine-grained quality differences. We show that by selecting the highest quality sentence pairs in the training data, we can improve translation quality while reducing the training size by half. We also provide a detailed analysis of the filtering results, which highlights the differences between both approaches.