EASLT: Emotion-Aware Sign Language Translation

TL;DR

EASLT introduces an emotion-aware framework for sign language translation that emphasizes facial expressions as key semantic indicators, significantly improving translation accuracy over prior gloss-free methods.

Contribution

The paper presents a novel emotion-aware model with a dedicated affective encoder and fusion module, enhancing semantic understanding in sign language translation.

Findings

Achieves state-of-the-art BLEU-4 scores on benchmarks

Effectively decouples affective semantics from manual gestures

Improves translation fidelity through emotion modeling

Abstract

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.