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    <abstract>We propose a unified architecture for jointly modeling Translation Quality Estimation (QE) and Automatic Post-Editing (APE) within a single lightweight language model. Our approach integrates quality prediction and correction generation in a single decoding process using a decoder-only Qwen2.5 model (0.5B parameters), augmented with a dedicated QE regression head operating on hidden states at a special token position. The model produces structured outputs that include a continuous quality score, an edit decision, and a corrected translation when necessary. We train on datasets of 100K, 1M, and 1.84M manually annotated samples across eight language pairs, enabling analysis of both data scale and distribution. Experimental results show that the proposed model achieves strong QE performance (r=0.907) and high post-editing decision accuracy (88.4%), while reducing over-editing compared to both autoregressive baselines and large commercial LLMs.</abstract>
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%0 Conference Proceedings
%T Predict and Fix: A Unified Model for Translation Quality Estimation and Post-Editing
%A Modrzejewski, Maciej
%A Bhaskar, Yash
%A Pateria, Chinmay
%Y Briakou, Eleftheria
%Y Gwinnup, Jeremy
%Y Goel, Shivali
%S Proceedings of the 17th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)
%D 2026
%8 August
%I Association for Machine Translation in the Americas
%C Québec City, Canada
%F modrzejewski-etal-2026-predict
%X We propose a unified architecture for jointly modeling Translation Quality Estimation (QE) and Automatic Post-Editing (APE) within a single lightweight language model. Our approach integrates quality prediction and correction generation in a single decoding process using a decoder-only Qwen2.5 model (0.5B parameters), augmented with a dedicated QE regression head operating on hidden states at a special token position. The model produces structured outputs that include a continuous quality score, an edit decision, and a corrected translation when necessary. We train on datasets of 100K, 1M, and 1.84M manually annotated samples across eight language pairs, enabling analysis of both data scale and distribution. Experimental results show that the proposed model achieves strong QE performance (r=0.907) and high post-editing decision accuracy (88.4%), while reducing over-editing compared to both autoregressive baselines and large commercial LLMs.
%U https://aclanthology.org/2026.amta-research.5/
%P 80-89

Markdown (Informal)

[Predict and Fix: A Unified Model for Translation Quality Estimation and Post-Editing](https://aclanthology.org/2026.amta-research.5/) (Modrzejewski et al., AMTA 2026)

• Predict and Fix: A Unified Model for Translation Quality Estimation and Post-Editing (Modrzejewski et al., AMTA 2026)

ACL

• Maciej Modrzejewski, Yash Bhaskar, and Chinmay Pateria. 2026. Predict and Fix: A Unified Model for Translation Quality Estimation and Post-Editing. In Proceedings of the 17th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), pages 80–89, Québec City, Canada. Association for Machine Translation in the Americas.