Machine translation quality estimation (QE) typically relies on dedicated neural models trained on human judgments. We evaluate whether cosine similarity over general-purpose embeddings can serve as a lightweight alternative, using Gemini embeddings as the scoring backbone. Through three experiments (rogue dimension analysis, score calibration, and a learned calibration head) and a root cause analysis, we find that cosine similarity between source and translation saturates in the 0.94–0.99 range because even poor translations preserve most of the source semantics, leaving an Area Under the ROC Curve (AUC) ceiling of approximately 0.63. However, a LightGBM classifier trained on normalized cosine and surface-level text features breaks through this ceiling (AUC 0.751), with the improvement driven primarily by features orthogonal to embedding similarity.

Post-Editing (PE) is typically performed on isolated segments or small batches, without access to broader document context. In this paper, we investigate whether pre-generated, document-level summaries can improve PE quality. Using a purpose-built summarization prompt evaluated across nine LLMs from OpenAI and Google, we select two models with contrasting summary styles for downstream experiments on 448 documents covering 37 target locales and 13 content domains. Summaries generated by gemini-2.5-flash-lite, which are directive and domain-specific, yield gains in edit distance and modest gains in COMET, whereas those generated by GPT-4o, which tend to be more generic and descriptive, degrade performance across most metrics. The positive effect appears most pronounced in terminologically dense domains and lower-resource locales. A qualitative analysis shows that improvements arise when summaries provide specific, actionable guidance on terminology, domain conventions, and style, and that performance decreases when summaries are underspecified or conflicting. These findings suggest that summary specificity and actionability, rather than the mere addition of context, determine whether document-level information benefits post-editing.

This paper discusses the capabilities and benefits of OPAL Enable, an advanced AI suite designed to modernize localization processes. The suite comprises Machine Translation, AI Post-Editing, and AI Quality Estimation tools, integrated into renowned translation management systems. The paper provides an in-depth analysis of these features, detailing their procedural order, and the time and cost savings they offer. It emphasizes the customization potential of OPAL Enable to meet client-specific requirements, increase scalability, and expedite workflows.

Automatic post-editing (APE) aims to correct errors in machine-translated text, enhancing translation quality, while reducing the need for human intervention. Despite advances in neural machine translation (NMT), the development of effective APE systems has been hindered by the lack of large-scale multilingual datasets specifically tailored to NMT outputs. To address this gap, we present and release LangMark, a new human-annotated multilingual APE dataset for English translation to seven languages: Brazilian Portuguese, French, German, Italian, Japanese, Russian, and Spanish. The dataset has 206,983 triplets, with each triplet consisting of a source segment, its NMT output, and a human post-edited translation. Annotated by expert human linguists, our dataset offers both linguistic diversity and scale. Leveraging this dataset, we empirically show that Large Language Models (LLMs) with few-shot prompting can effectively perform APE, improving upon leading commercial and even proprietary machine translation systems. We believe that this new resource will facilitate the future development and evaluation of APE systems.