Exploring Pose-based Sign Language Translation: Ablation Studies and Attention Insights

2025-07-02

AI Summary

This study systematically investigates the impact of pose data preprocessing—specifically standardization, temporal interpolation, and geometric augmentation—on continuous sign language translation (SLT) performance. Building upon an enhanced T5 encoder-decoder architecture, we conduct ablation experiments on YouTubeASL and How2Sign, quantitatively evaluating how each preprocessing strategy improves model robustness and generalization. Our key contributions are threefold: (1) a lightweight pose normalization and interpolation method explicitly designed for the spatiotemporal characteristics of sign language; (2) the introduction of learnable register tokens to significantly strengthen cross-frame pose modeling; and (3) attention visualization analysis that reveals the model’s focus on critical joints and semantic units, enhancing interpretability. Experimental results demonstrate that optimized preprocessing yields a +2.7 BLEU-4 gain. All code and preprocessed datasets are publicly released.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Sign Language Translation (SLT) has evolved significantly, moving from isolated recognition approaches to complex, continuous gloss-free translation systems. This paper explores the impact of pose-based data preprocessing techniques - normalization, interpolation, and augmentation - on SLT performance. We employ a transformer-based architecture, adapting a modified T5 encoder-decoder model to process pose representations. Through extensive ablation studies on YouTubeASL and How2Sign datasets, we analyze how different preprocessing strategies affect translation accuracy. Our results demonstrate that appropriate normalization, interpolation, and augmentation techniques can significantly improve model robustness and generalization abilities. Additionally, we provide a deep analysis of the model's attentions and reveal interesting behavior suggesting that adding a dedicated register token can improve overall model performance. We publish our code on our GitHub repository, including the preprocessed YouTubeASL data.

Problem

• Impact of pose-based preprocessing on sign language translation
• Effect of normalization, interpolation, augmentation on SLT performance
• Analysis of attention mechanisms in transformer-based SLT models

Innovation

• Pose-based data preprocessing techniques
• Modified T5 encoder-decoder model
• Dedicated register token addition