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. |
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| Challenge: | A carefully engineered ensemble of pre-trained multilingual language models won the QE shared task at WMT19. |
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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
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| Challenge: | Existing automated tools are not good enough to evaluate translation quality . existing tools are often accused of having low reliability and agreement . |
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