# Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task

arXiv: https://arxiv.org/abs/1907.10352 · HTML: https://mlsanity.com/paper/1907.10352/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 13 of 13)

- [Quality Estimation without Human-labeled Data](https://mlsanity.com/paper/2102.04020/) (2021)
- [From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation](https://mlsanity.com/paper/2403.14118/) (2024)
- [DirectQE: Direct Pretraining for Machine Translation Quality Estimation](https://mlsanity.com/paper/2105.07149/) (2021)
- [Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation](https://mlsanity.com/paper/2109.07141/) (2021)
- [QEMind: Alibaba's Submission to the WMT21 Quality Estimation Shared Task](https://mlsanity.com/paper/2112.14890/) (2021)
- [Ensemble Fine-tuned mBERT for Translation Quality Estimation](https://mlsanity.com/paper/2109.03914/) (2021)
- [Learning Confidence for Transformer-based Neural Machine Translation](https://mlsanity.com/paper/2203.11413/) (2022)
- [Multi-Target Cross-Lingual Summarization: a novel task and a language-neutral approach](https://mlsanity.com/paper/2410.00502/) (2024)
- [Verdi: Quality Estimation and Error Detection for Bilingual Corpora](https://mlsanity.com/paper/2105.14878/) (2021)
- [Mismatching-Aware Unsupervised Translation Quality Estimation For Low-Resource Languages](https://mlsanity.com/paper/2208.00463/) (2022)
- [SurreyAI 2023 Submission for the Quality Estimation Shared Task](https://mlsanity.com/paper/2312.00525/) (2023)
- [Ensemble-based Transfer Learning for Low-resource Machine Translation Quality Estimation](https://mlsanity.com/paper/2105.07622/) (2021)
- [Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications](https://mlsanity.com/paper/2109.08627/) (2021)

## Abstract (arXiv)

We present the contribution of the Unbabel team to the WMT 2019 Shared Task on Quality Estimation. We participated on the word, sentence, and document-level tracks, encompassing 3 language pairs: English-German, English-Russian, and English-French. Our submissions build upon the recent OpenKiwi framework: we combine linear, neural, and predictor-estimator systems with new transfer learning approaches using BERT and XLM pre-trained models. We compare systems individually and propose new ensemble techniques for word and sentence-level predictions. We also propose a simple technique for converting word labels into document-level predictions. Overall, our submitted systems achieve the best results on all tracks and language pairs by a considerable margin.