Tibetan-Chinese Machine Translation Enhanced on Cross-Lingual Pre-Trained Model

Tibetan-Chinese machine translation has become a focal point of interest within the Tibetan community due to its importance for effective communication and cultural preservation. Although technological advancements have been made, current Tibetan-Chinese translation systems still fall short of satisfactory performance. Moreover, evaluating these systems using a publicly available and reliable benchmark dataset remains problematic. To address these challenges, we propose an enhanced TibetanChinese machine translation framework. This framework includes data filtering techniques and utilizes the semantic knowledge of the state-of-the-art Chinese cross-lingual model, CINO. After preprocessing, we trained our model using the Transformer architecture and found that our approach significantly improves translation performance. Additionally, we have developed a highquality, expert-reviewed test dataset to support community evaluations of translation systems. The details of our experimental model and the test dataset can be accessed at <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>1https://github.com/UTibetNLP/Ti-ChTrans.