The main challenge of fully non-autoregressive translation (Fully NAT) lies in maintaining translation quality comparable to autoregressive translation (AT) while preserving the decoding speed advantage. This challenge arises from the parallel decoding nature, which prevents Fully NAT methods from capturing dependency information on the target side, leading to degraded translation quality. Therefore, enhancing the model’s ability to capture dependency information from the source side is a natural approach, especially given that syntactic information has proven effective in improving AT methods. Although significant progress has been made in this area in recent years, there has been limited research on incorporating syntactic information in Fully NAT. Through experiments on five translation benchmarks (e.g., workshop on machine translation, WMT), it is found that dependency syntax information is highly beneficial for fully NAT methods, significantly improving translation performance with a decoding speed cost that remains within an acceptable range. The code has been released at https://github.com/tianxiexiaozhu77/syngec.