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.

手语转文字是一项关键技术，可以消除听障者之间的交流障碍。我们在最近发表的研究上进行复制并进行改进，通过使用 BLEU 和 rBLEU 度量来评估模型的翻译质量。在我们的消融研究中，我们发现模型的性能受到优化器、激活函数和标签平滑的显著影响。进一步的研究旨在改进视觉特征捕捉、增强解码器的利用以及整合预训练解码器以获得更好的翻译结果。我们的源代码可供查看以便复制我们的结果并鼓励未来研究。