Dictionary-Injected Pre-training for Traditional Mongolian-Chinese Machine Translation

In recent years, pretrained models have achieved remarkable progress in the field of natural language processing and have demonstrated superior performance across a variety of machine translation tasks. However, for the low-resource language pair of Traditional Mongolian and Chinese, the transfer capability of mainstream multilingual pretrained models is substantially limited, primarily because Traditional Mongolian is not covered in their pretraining corpora. To address this issue, this paper proposes a dictionary-injected pretraining method. Specifically, a constructed Mongolian–Chinese bilingual dictionary is used to perform lexical replacement on Chinese monolingual corpora, thereby generating mixed texts containing Traditional Mongolian. The model is then pretrained with a BART-style denoising autoencoding objective, enabling it to recover the original Chinese sentences under cross-lingual perturbations. In this way, the proposed method enhances the model’s cross-lingual semantic understanding and improves Mongolian–Chinese machine translation performance. Experimental results show that, on the test sets for both Mongolian-to-Chinese and Chinese-to-Mongolian translation, the proposed method outperforms a strong BART baseline by 2.3 and 2.4 BLEU points, respectively, thereby confirming its effectiveness for Mongolian–Chinese machine translation tasks.