mlsanityData to 14 Oct 2026

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

arXiv 2311.05350 ↗First posted 9 Nov 2023cs.CLcited by 19

Not on the map. Why?

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.

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

13 of 60 references.

References · 13

  1. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
  2. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
  3. 2020COMET: A Neural Framework for MT Evaluationcited by 1,033
  4. 2017A Closer Look at Memorization in Deep Networkscited by 942
  5. 2020What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimationcited by 497
  6. 2022CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Taskcited by 207
  7. 2020Unsupervised Domain Clusters in Pretrained Language Modelscited by 160
  8. 2018On the Impact of Various Types of Noise on Neural Machine Translationcited by 76
  9. 2017Learning Joint Multilingual Sentence Representations with Neural Machine Translationcited by 52
  10. 2019OpenKiwi: An Open Source Framework for Quality Estimationcited by 49
  11. 2018Dual Conditional Cross-Entropy Filtering of Noisy Parallel Corporacited by 40
  12. 2019Low-Resource Corpus Filtering using Multilingual Sentence Embeddingscited by 25
1 more
  1. 2018Microsoft's Submission to the WMT2018 News Translation Task: How I Learned to Stop Worrying and Love the Datacited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 11; 2025: 6; 2026: 2.

19 papers.

Sort:

By area:

  1. 2024Navigating the Metrics Maze: Reconciling Score Magnitudes and Accuraciescited by 40
  2. 2024A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelismcited by 35
  3. 2025Enhancing Multilingual LLM Pretraining with Model-Based Data Selectioncited by 35
  4. 2025Granary: Speech Recognition and Translation Dataset in 25 European Languagescited by 22
  5. 2024Introducing the NewsPaLM MBR and QE Dataset: LLM-Generated High-Quality Parallel Data Outperforms Traditional Web-Crawled Datacited by 10
  6. 2024Pitfalls and Outlooks in Using COMETcited by 10
  7. 2024Beyond Correlation: Interpretable Evaluation of Machine Translation Metricscited by 10
  8. 2024Watching the Watchers: Exposing Gender Disparities in Machine Translation Quality Estimationcited by 10
  9. 2025Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question Answeringcited by 10
  10. 2024xCOMET-lite: Bridging the Gap Between Efficiency and Quality in Learned MT Evaluation Metricscited by 7
  11. 2024Recovering document annotations for sentence-level bitextcited by 6
  12. 2025Adding Chocolate to Mint: Mitigating Metric Interference in Machine Translationcited by 6
7 more
  1. 2024Cross-lingual Human-Preference Alignment for Neural Machine Translation with Direct Quality Optimizationcited by 5
  2. 2024How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise in Machine Translationcited by 4
  3. 2024PromptOptMe: Error-Aware Prompt Compression for LLM-based MT Evaluation Metricscited by 4
  4. 2025Are Generative Models Underconfident? Better Quality Estimation with Boosted Model Probabilitycited by 4
  5. 2025Hacking Neural Evaluation Metrics with Single Hub Textcited by 1
  6. 2026FairQE: Multi-Agent Framework for Mitigating Gender Bias in Translation Quality Estimationcited by 1
  7. 2026Model-Based Quality Assessment for Massively Multilingual Parallel Datacited by 0