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.

The advent of Large Language Models (LLMs) has significantly transformed the localization sector. This presentation examines the integration of Generative AI (GenAI) solutions into translation and localization workflows, focusing on Automated Post-Editing (APE) and Automated Translation Error Detection. Using language pairs English-German and English-Japanese, APE consistently enhances translation quality by an average of 2-5 BLEU and 0.1-0.25 COMET compared to strong generic baselines. For specialized domains, APE reduces post-editing time by 40% for the worst-performing outputs from encoder-decoder-based MT systems. Combining APE with our in-house reference-free Quality Estimation (QE) model yields additional improvement. Through detailed methodologies, human evaluation results, and industrial applications, we demonstrate the transformative potential of these technologies in enhancing accuracy, reducing costs, and optimizing localization processes.

The correct translation of named entities (NEs) still poses a challenge for conventional neural machine translation (NMT) systems. This study explores methods incorporating named entity recognition (NER) into NMT with the aim to improve named entity translation. It proposes an annotation method that integrates named entities and inside–outside–beginning (IOB) tagging into the neural network input with the use of source factors. Our experiments on English→German and English→ Chinese show that just by including different NE classes and IOB tagging, we can increase the BLEU score by around 1 point using the standard test set from WMT2019 and achieve up to 12% increase in NE translation rates over a strong baseline.