Transferring Zero-shot Multilingual Chinese-Chinese Translation Model for Chinese Minority Language Translation

Transfer learning is an effective method to improve the performance of low-resource translation, but its effectiveness heavily relies on specific languages, and transferring between similar languages usually leads to better results. Large-scale English (en) parallel data is normally easier to obtain for Chinese (zh) than the other languages, but when translating Chinese minority languages into Chinese Han, there is a large discrepancy between English and Chinese minority languages, and transferring from Chinese Han seems to be a better choice than from English. As Chinese Han is also the target language, training a zh→zh translation model is problematic due to the lack of such parallel data. In this paper, we propose to obtain the zh→zh translation model using Multilingual Neural Machine Translation (MNMT) by involving Chinese in both the source and the target sides of different translation tasks, and using the zero-shot zhOur experiments on the CCMT 2023 Chinese minority language→zh translation direction for transfer learning. translation tasks show that transferring from the zh→zh mode can lead to significant improvements (+1.49, +4.65, +0 82 lBLEU on the Tibetan (ti), Mongolian (mn) and Uyghur (uy) to Chinese Han tasks respectively) than from the en→zh model.