# QEMind: Alibaba's Submission to the WMT21 Quality Estimation Shared Task

arXiv: https://arxiv.org/abs/2112.14890 · HTML: https://mlsanity.com/paper/2112.14890/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 30 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://mlsanity.com/paper/2008.00401/) (2020)
- [Unsupervised Quality Estimation for Neural Machine Translation](https://mlsanity.com/paper/2005.10608/) (2020)
- [TransQuest at WMT2020: Sentence-Level Direct Assessment](https://mlsanity.com/paper/2010.05318/) (2020)
- [Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task](https://mlsanity.com/paper/1907.10352/) (2019)
- ["Bilingual Expert" Can Find Translation Errors](https://mlsanity.com/paper/1807.09433/) (2018)
- [Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation](https://mlsanity.com/paper/2109.07141/) (2021)
- [Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing](https://mlsanity.com/paper/2009.09126/) (2020)

## Built on by (most cited first, 4 of 4)

- [AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages](https://mlsanity.com/paper/2311.09828/) (2023)
- [Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task](https://mlsanity.com/paper/2210.10049/) (2022)
- [Mismatching-Aware Unsupervised Translation Quality Estimation For Low-Resource Languages](https://mlsanity.com/paper/2208.00463/) (2022)
- [Unsupervised Translation Quality Estimation Exploiting Synthetic Data and Pre-trained Multilingual Encoder](https://mlsanity.com/paper/2311.05117/) (2023)

## Abstract (arXiv)

Quality Estimation, as a crucial step of quality control for machine translation, has been explored for years. The goal is to investigate automatic methods for estimating the quality of machine translation results without reference translations. In this year's WMT QE shared task, we utilize the large-scale XLM-Roberta pre-trained model and additionally propose several useful features to evaluate the uncertainty of the translations to build our QE system, named textitQEMind. The system has been applied to the sentence-level scoring task of Direct Assessment and the binary score prediction task of Critical Error Detection. In this paper, we present our submissions to the WMT 2021 QE shared task and an extensive set of experimental results have shown us that our multilingual systems outperform the best system in the Direct Assessment QE task of WMT 2020.