# Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task

arXiv: https://arxiv.org/abs/2210.10049 · HTML: https://mlsanity.com/paper/2210.10049/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [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)
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021)
- [InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training](https://mlsanity.com/paper/2007.07834/) (2020)
- [TransQuest: Translation Quality Estimation with Cross-lingual Transformers](https://mlsanity.com/paper/2011.01536/) (2020)
- [Uncertainty-Aware Machine Translation Evaluation](https://mlsanity.com/paper/2109.06352/) (2021)
- [UniTE: Unified Translation Evaluation](https://mlsanity.com/paper/2204.13346/) (2022)
- [Self-Paced Learning for Neural Machine Translation](https://mlsanity.com/paper/2010.04505/) (2020)
- [Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation](https://mlsanity.com/paper/2204.06812/) (2022)
- [Frequency-Aware Contrastive Learning for Neural Machine Translation](https://mlsanity.com/paper/2112.14484/) (2021)
- [Towards User-Driven Neural Machine Translation](https://mlsanity.com/paper/2106.06200/) (2021)
- [RoBLEURT Submission for the WMT2021 Metrics Task](https://mlsanity.com/paper/2204.13352/) (2022)
- [KGR^4: Retrieval, Retrospect, Refine and Rethink for Commonsense Generation](https://mlsanity.com/paper/2112.08266/) (2021)
- [QEMind: Alibaba's Submission to the WMT21 Quality Estimation Shared Task](https://mlsanity.com/paper/2112.14890/) (2021)

## Built on by (most cited first, 3 of 3)

- [MTUncertainty: Assessing the Need for Post-editing of Machine Translation Outputs by Fine-tuning OpenAI LLMs](https://mlsanity.com/paper/2308.00158/) (2023)
- [SurreyAI 2023 Submission for the Quality Estimation Shared Task](https://mlsanity.com/paper/2312.00525/) (2023)
- [Efficient Machine Translation Corpus Generation: Integrating Human-in-the-Loop Post-Editing with Large Language Models](https://mlsanity.com/paper/2502.12755/) (2025)

## Abstract (arXiv)

In this paper, we present our submission to the sentence-level MQM benchmark at Quality Estimation Shared Task, named UniTE (Unified Translation Evaluation). Specifically, our systems employ the framework of UniTE, which combined three types of input formats during training with a pre-trained language model. First, we apply the pseudo-labeled data examples for the continuously pre-training phase. Notably, to reduce the gap between pre-training and fine-tuning, we use data pruning and a ranking-based score normalization strategy. For the fine-tuning phase, we use both Direct Assessment (DA) and Multidimensional Quality Metrics (MQM) data from past years' WMT competitions. Finally, we collect the source-only evaluation results, and ensemble the predictions generated by two UniTE models, whose backbones are XLM-R and InfoXLM, respectively. Results show that our models reach 1st overall ranking in the Multilingual and English-Russian settings, and 2nd overall ranking in English-German and Chinese-English settings, showing relatively strong performances in this year's quality estimation competition.