Sign language to text is a crucial technology that can break down communication barriers for individuals with hearing difficulties. We replicate and try to improve on a recently published study. We evaluate models using BLEU and rBLEU metrics to ensure translation quality. During our ablation study, we found that the model's performance is significantly influenced by optimizers, activation functions, and label smoothing. Further research aims to refine visual feature capturing, enhance decoder utilization, and integrate pre-trained decoders for better translation outcomes. Our source code is available to facilitate replication of our results and encourage future research.

翻译：手语到文本是一项关键的技术，能够消除听力障碍人士的沟通障碍。我们复现并尝试改进近期发表的一项研究。使用BLEU和rBLEU指标评估模型以确保翻译质量。在消融实验中，我们发现优化器、激活函数和标签平滑对模型性能有显著影响。未来研究旨在优化视觉特征捕捉、提升解码器利用率，并整合预训练解码器以获得更好的翻译效果。我们公开源代码以促进结果复现并鼓励未来研究。