XLNet's Strategy for Dealing with Language Structure Differences in Multilingual Translation Systems

In order to solve the problems of semantic deviation and syntactic errors caused by language structure differences in multilingual translation systems, this paper uses the global autoregressive property of XLNet to jointly encode the source language and the target language, and enhances the robustness of the model to word order changes by permuting word order training. A cross-language attention alignment module is designed and language-specific position encoding is introduced. Finally, multi-task learning is used to jointly optimize the translation quality and language structure reconstruction loss. XLNet outperforms XLM-R in BLEU, structural error rate, and ChrF, with an average BLEU value of 91%, showing a lower structural error rate. This paper provides a more efficient and accurate translation tool for cross-language communication.