Recently, diffusion models have excelled in image generation tasks and have
also been applied to neural language processing (NLP) for controllable text
generation. However, the application of diffusion models in a cross-lingual
setting is less unexplored. Additionally, while pretraining with diffusion
models has been studied within a single language, the potential of
cross-lingual pretraining remains understudied. To address these gaps, we
propose XDLM, a novel Cross-lingual diffusion model for machine translation,
consisting of pretraining and fine-tuning stages. In the pretraining stage, we
propose TLDM, a new training objective for mastering the mapping between
different languages; in the fine-tuning stage, we build up the translation
system based on the pretrained model. We evaluate the result on several machine
translation benchmarks and outperformed both diffusion and Transformer
baselines.

提出了 XDLM，一种新颖的用于机器翻译的跨语言扩散模型，包括预训练和微调阶段。在预训练阶段，提出了 TLDM，用于掌握不同语言之间的映射关系；在微调阶段，基于预训练模型构建了翻译系统，并在多个机器翻译基准测试中优于扩散模型和 Transformer 基线。