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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Enhancing Localization Workflows with GenAI-Based Solutions: A Deep Dive into Automated Post-Editing and Translation Error Detection
%A Modrzejewski, Maciej
%Y Martindale, Marianna
%Y Campbell, Janice
%Y Savenkov, Konstantin
%Y Goel, Shivali
%S Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 2: Presentations)
%D 2024
%8 September
%I Association for Machine Translation in the Americas
%C Chicago, USA
%F modrzejewski-2024-enhancing
%X 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.
%U https://aclanthology.org/2024.amta-presentations.8
%P 116-132

Markdown (Informal)

[Enhancing Localization Workflows with GenAI-Based Solutions: A Deep Dive into Automated Post-Editing and Translation Error Detection](https://aclanthology.org/2024.amta-presentations.8) (Modrzejewski, AMTA 2024)

• Enhancing Localization Workflows with GenAI-Based Solutions: A Deep Dive into Automated Post-Editing and Translation Error Detection (Modrzejewski, AMTA 2024)

ACL

• Maciej Modrzejewski. 2024. Enhancing Localization Workflows with GenAI-Based Solutions: A Deep Dive into Automated Post-Editing and Translation Error Detection. In Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 2: Presentations), pages 116–132, Chicago, USA. Association for Machine Translation in the Americas.