In this paper, we present our submitted model for translating ancient to modern texts, which ranked sixth in the closed track of ancient Chinese in the 2nd International Review of Automatic Analysis of Ancient Chinese (EvaHan). Specifically, we employed two strategies to improve the translation from ancient to modern texts. First, we used bpe-drop to enhance the parallel corpus. Second, we use SikuRoBERTa to simultaneously initialize the translation model’s codec and reconstruct the bpe word list. In our experiments, we compare the baseline model, rdrop, pre-trained model, and parameter initialization methods. The experimental results show that the parameter initialization method in this paper significantly outperforms the baseline model in terms of performance, and its BLEU score reaches 21.75.