Papers with Quality estimation
Early-Exit and Instant Confidence Translation Quality Estimation (2026.eacl-long)
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| Challenge: | Quality estimation models are often opaque and computationally expensive, making them impractical to be part of large-scale pipelines. |
| Approach: | They propose an uncertainty-aware quality estimation model that matches previous approaches at a fraction of their costs. |
| Outcome: | The proposed method reduces evaluation costs by 50% and improves reranking performance. |
Quality Estimation without Human-labeled Data (2021.eacl-main)
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Yi-Lin Tuan, Ahmed El-Kishky, Adithya Renduchintala, Vishrav Chaudhary, Francisco Guzmán, Lucia Specia
| Challenge: | Quality estimation aims to measure the quality of translated content without access to a reference translation. |
| Approach: | They propose a method that uses synthetic training data to train supervised quality estimation models. |
| Outcome: | The proposed model outperforms models trained on human-annotated data for sentence and word-level prediction. |
Self-Supervised Quality Estimation for Machine Translation (2021.emnlp-main)
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Yuanhang Zheng, Zhixing Tan, Meng Zhang, Mieradilijiang Maimaiti, Huanbo Luan, Maosong Sun, Qun Liu, Yang Liu
| Challenge: | Training QE models require massive parallel data with hand-crafted quality annotations, which are time-consuming and labor-intensive to obtain. |
| Approach: | They propose a self-supervised method to evaluate machine-translated sentences without references by recovering masked target words. |
| Outcome: | The proposed method outperforms previous unsupervised methods on several QE tasks in different language pairs and domains. |
Watching the Watchers: Exposing Gender Disparities in Machine Translation Quality Estimation (2025.acl-long)
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| Challenge: | Qualitative estimation (QE) metrics have been optimized to align with human quality judgments, but whether they encode social biases has been largely overlooked. |
| Approach: | They define and investigate gender bias of QE metrics and discuss its downstream implications for machine translation (MT) when a human entity’s gender in the source is undisclosed, masculine-inflected translations score higher than feminine-infflectes translations are penalized. |
| Outcome: | The proposed measures are based on gender-based quality estimation metrics across multiple domains, datasets, and languages. |