# TransQuest at WMT2020: Sentence-Level Direct Assessment

arXiv: https://arxiv.org/abs/2010.05318 · HTML: https://mlsanity.com/paper/2010.05318/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2020.

## Built on by (most cited first, 20 of 20)

- [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://mlsanity.com/paper/2209.06243/) (2022)
- [Uncertainty-Aware Machine Translation Evaluation](https://mlsanity.com/paper/2109.06352/) (2021)
- [The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Results](https://mlsanity.com/paper/2110.04392/) (2021)
- [Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors](https://mlsanity.com/paper/2110.04399/) (2021)
- [Translation Error Detection as Rationale Extraction](https://mlsanity.com/paper/2108.12197/) (2021)
- [T5Score: Discriminative Fine-tuning of Generative Evaluation Metrics](https://mlsanity.com/paper/2212.05726/) (2022)
- [An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers](https://mlsanity.com/paper/2106.00143/) (2021)
- [USCORE: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation](https://mlsanity.com/paper/2202.10062/) (2022)
- [Towards Explainable Evaluation Metrics for Machine Translation](https://mlsanity.com/paper/2306.13041/) (2023)
- [PreQuEL: Quality Estimation of Machine Translation Outputs in Advance](https://mlsanity.com/paper/2205.09178/) (2022)
- [What do Large Language Models Need for Machine Translation Evaluation?](https://mlsanity.com/paper/2410.03278/) (2024)
- [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)
- [Levenshtein Training for Word-level Quality Estimation](https://mlsanity.com/paper/2109.05611/) (2021)
- [Giving the Old a Fresh Spin: Quality Estimation-Assisted Constrained Decoding for Automatic Post-Editing](https://mlsanity.com/paper/2501.17265/) (2025)
- [Quality Estimation Using Round-trip Translation with Sentence Embeddings](https://mlsanity.com/paper/2111.00554/) (2021)
- [SurreyAI 2023 Submission for the Quality Estimation Shared Task](https://mlsanity.com/paper/2312.00525/) (2023)
- [Enhancing Plagiarism Detection in Marathi with a Weighted Ensemble of TF-IDF and BERT Embeddings for Low-Resource Language Processing](https://mlsanity.com/paper/2501.05260/) (2025)
- [MDQE: A More Accurate Direct Pretraining for Machine Translation Quality Estimation](https://mlsanity.com/paper/2107.14600/) (2021)
- [Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications](https://mlsanity.com/paper/2109.08627/) (2021)

## Abstract (arXiv)

This paper presents the team TransQuest's participation in Sentence-Level Direct Assessment shared task in WMT 2020. We introduce a simple QE framework based on cross-lingual transformers, and we use it to implement and evaluate two different neural architectures. The proposed methods achieve state-of-the-art results surpassing the results obtained by OpenKiwi, the baseline used in the shared task. We further fine tune the QE framework by performing ensemble and data augmentation. Our approach is the winning solution in all of the language pairs according to the WMT 2020 official results.