# TransQuest: Translation Quality Estimation with Cross-lingual Transformers

arXiv: https://arxiv.org/abs/2011.01536 · HTML: https://mlsanity.com/paper/2011.01536/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 30 of 59)

- [Quality-Aware Decoding for Neural Machine Translation](https://mlsanity.com/paper/2205.00978/) (2022)
- [Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection](https://mlsanity.com/paper/2301.07779/) (2023)
- [UniTE: Unified Translation Evaluation](https://mlsanity.com/paper/2204.13346/) (2022)
- [AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages](https://mlsanity.com/paper/2311.09828/) (2023)
- [Translation Error Detection as Rationale Extraction](https://mlsanity.com/paper/2108.12197/) (2021)
- [Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors](https://mlsanity.com/paper/2310.16924/) (2023)
- [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)
- [MENLI: Robust Evaluation Metrics from Natural Language Inference](https://mlsanity.com/paper/2208.07316/) (2022)
- [USCORE: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation](https://mlsanity.com/paper/2202.10062/) (2022)
- [Towards Explainable Evaluation Metrics for Natural Language Generation](https://mlsanity.com/paper/2203.11131/) (2022)
- [The Inside Story: Towards Better Understanding of Machine Translation Neural Evaluation Metrics](https://mlsanity.com/paper/2305.11806/) (2023)
- [Towards Explainable Evaluation Metrics for Machine Translation](https://mlsanity.com/paper/2306.13041/) (2023)
- [Don't Rank, Combine! Combining Machine Translation Hypotheses Using Quality Estimation](https://mlsanity.com/paper/2401.06688/) (2024)
- [From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation](https://mlsanity.com/paper/2403.14118/) (2024)
- [MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language](https://mlsanity.com/paper/2406.13698/) (2024)
- [Disentangling Uncertainty in Machine Translation Evaluation](https://mlsanity.com/paper/2204.06546/) (2022)
- [Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Model](https://mlsanity.com/paper/2310.06707/) (2023)
- [A review of faithfulness metrics for hallucination assessment in Large Language Models](https://mlsanity.com/paper/2501.00269/) (2024)
- [Better Quality Estimation for Low Resource Corpus Mining](https://mlsanity.com/paper/2203.08259/) (2022)
- [Alibaba-Translate China's Submission for WMT 2022 Metrics Shared Task](https://mlsanity.com/paper/2210.09683/) (2022)
- [When LLMs Struggle: Reference-less Translation Evaluation for Low-resource Languages](https://mlsanity.com/paper/2501.04473/) (2025)
- [What do Large Language Models Need for Machine Translation Evaluation?](https://mlsanity.com/paper/2410.03278/) (2024)
- [Knowledge Distillation for Quality Estimation](https://mlsanity.com/paper/2107.00411/) (2021)
- [Adam Mickiewicz University at WMT 2022: NER-Assisted and Quality-Aware Neural Machine Translation](https://mlsanity.com/paper/2209.02962/) (2022)
- [Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation](https://mlsanity.com/paper/2109.10859/) (2021)
- [Tailoring Domain Adaptation for Machine Translation Quality Estimation](https://mlsanity.com/paper/2304.08891/) (2023)
- [Multi-Dimensional Machine Translation Evaluation: Model Evaluation and Resource for Korean](https://mlsanity.com/paper/2403.12666/) (2024)
- [TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation](https://mlsanity.com/paper/2104.04632/) (2021)
- [How should human translation coexist with NMT? Efficient tool for building high quality parallel corpus](https://mlsanity.com/paper/2111.00191/) (2021)

## Abstract (arXiv)

Recent years have seen big advances in the field of sentence-level quality estimation (QE), largely as a result of using neural-based architectures. However, the majority of these methods work only on the language pair they are trained on and need retraining for new language pairs. This process can prove difficult from a technical point of view and is usually computationally expensive. In this paper we propose a simple QE framework based on cross-lingual transformers, and we use it to implement and evaluate two different neural architectures. Our evaluation shows that the proposed methods achieve state-of-the-art results outperforming current open-source quality estimation frameworks when trained on datasets from WMT. In addition, the framework proves very useful in transfer learning settings, especially when dealing with low-resourced languages, allowing us to obtain very competitive results.