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.

Diego Velazquez, Mikaela Grace, Konstantinos Karageorgos, Lawrence Carin, Aaron Schliem, Dimitrios Zaikis, Roger Wechsler. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

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.

Moving objects pose a challenge to every video stabilization algorithm. We present a novel, efficient filtering technique that manages to remove outlier motion vectors caused from moving objects in a per-pixel smoothing setting. We leverage semantic information to change the calculation of optical flow, forcing the outliers to reside in the edges of our semantic mask. After a `content-preserving warping' and a smoothing step we manage to produce stable and artifact-free videos.