标题： EASLT：情绪感知的手语翻译 显示英文标题

标题： EASLT: Emotion-Aware Sign Language Translation

摘要： 手语翻译（SLT）是一项复杂的跨模态任务，需要整合手动信号（MS）和非手动信号（NMS）。 虽然最近的无词典SLT方法在翻译手动手势方面取得了进展，但它们经常忽视面部表情的语义重要性，导致当不同概念具有相同的手动表达时出现歧义。 为了解决这个问题，我们提出了**EASLT**（**E**motion-**A**ware **S**ign **L**anguage **T**ranslation），一种将面部情感视为稳健语义锚点而非辅助信息的框架。 与将面部表情置于次要角色的方法不同，EASLT引入了一个专门的情感编码器来捕捉连续的情感动态。 这些表示通过一种新颖的*情感感知融合*（EAF）模块进行集成，该模块根据情感上下文自适应地重新校准时空手语特征，以解决语义歧义。 在PHOENIX14T和CSL-Daily基准上的广泛评估表明，EASLT在无词典方法中表现出色，分别获得了26.15和22.80的BLEU-4分数，以及61.0和57.8的BLEURT分数。 消融研究证实，显式建模情感有效地将情感语义与手动动态解耦，显著提高了翻译保真度。 代码可在https://github.com/TuGuobin/EASLT获取。 显示英文摘要

摘要： Sign Language Translation (SLT) is a complex cross-modal task requiring the integration of Manual Signals (MS) and Non-Manual Signals (NMS). While recent gloss-free SLT methods have made strides in translating manual gestures, they frequently overlook the semantic criticality of facial expressions, resulting in ambiguity when distinct concepts share identical manual articulations. To address this, we present **EASLT** (**E**motion-**A**ware **S**ign **L**anguage **T**ranslation), a framework that treats facial affect not as auxiliary information, but as a robust semantic anchor. Unlike methods that relegate facial expressions to a secondary role, EASLT incorporates a dedicated emotional encoder to capture continuous affective dynamics. These representations are integrated via a novel *Emotion-Aware Fusion* (EAF) module, which adaptively recalibrates spatio-temporal sign features based on affective context to resolve semantic ambiguities. Extensive evaluations on the PHOENIX14T and CSL-Daily benchmarks demonstrate that EASLT establishes advanced performance among gloss-free methods, achieving BLEU-4 scores of 26.15 and 22.80, and BLEURT scores of 61.0 and 57.8, respectively. Ablation studies confirm that explicitly modeling emotion effectively decouples affective semantics from manual dynamics, significantly enhancing translation fidelity. Code is available at https://github.com/TuGuobin/EASLT.