SiLVERScore: Semantically-Aware Embeddings for Sign Language Generation Evaluation

• URL: http://arxiv.org/abs/2509.03791v1
• Date: Thu, 04 Sep 2025 00:58:43 GMT
• Title: SiLVERScore: Semantically-Aware Embeddings for Sign Language Generation Evaluation
• Authors: Saki Imai, Mert İnan, Anthony Sicilia, Malihe Alikhani,
• Abstract summary: We propose SiLVERScore, a semantically-aware embedding-based evaluation metric for sign language generation.<n>On PHOENIX-14T and CSL-Daily datasets, SiLVERScore achieves near-perfect discrimination between correct and random pairs.
• Score: 29.960223851833785
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluating sign language generation is often done through back-translation, where generated signs are first recognized back to text and then compared to a reference using text-based metrics. However, this two-step evaluation pipeline introduces ambiguity: it not only fails to capture the multimodal nature of sign language-such as facial expressions, spatial grammar, and prosody-but also makes it hard to pinpoint whether evaluation errors come from sign generation model or the translation system used to assess it. In this work, we propose SiLVERScore, a novel semantically-aware embedding-based evaluation metric that assesses sign language generation in a joint embedding space. Our contributions include: (1) identifying limitations of existing metrics, (2) introducing SiLVERScore for semantically-aware evaluation, (3) demonstrating its robustness to semantic and prosodic variations, and (4) exploring generalization challenges across datasets. On PHOENIX-14T and CSL-Daily datasets, SiLVERScore achieves near-perfect discrimination between correct and random pairs (ROC AUC = 0.99, overlap < 7%), substantially outperforming traditional metrics.

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