  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.
