2025/10/22 by Yasser Hamidullah, Hamidullah, Yasser, Josef van Genabith +3 · 1 citation
Computer Science · Psychology · #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Hand Gesture Recognition Systems #Hearing Impairment and Communication #Human Pose and Action Recognition
paper · pdf · doi:10.48550/arxiv.2510.19413
openalex publication_date 2025/10/22 · openalex created_date 2025/10/24 · openalex updated_date 2026/07/28
This paper describes the DFKI-MLT submission to the WMT-SLT 2022 sign language translation (SLT) task from Swiss German Sign Language (video) into German (text). State-of-the-art techniques for SLT use a generic seq2seq architecture with customized input embeddings. Instead of word embeddings as used in textual machine translation, SLT systems use features extracted from video frames. Standard approaches often do not benefit from temporal features. In our participation, we present a system that learns spatio-temporal feature representations and translation in a single model, resulting in a real end-to-end architecture expected to better generalize to new data sets. Our best system achieved 5±1 BLEU points on the development set, but the performance on the test dropped to 0.11±0.06 BLEU points.