Pre-training model for low-resource Chinese–Braille translation

Abstract

The technology for converting Chinese to Braille is of great importance. When paired with a Braille display, it can better meet the educational and daily needs of the visually impaired community, especially children and students. Incorporating visual assistance mechanisms can further enhance the user experience and provide comprehensive support for individuals with visual impairments. In recent years, the use of end-to-end neural machine translation models for Chinese–Braille translation has gained traction. However, this task requires large, high-quality, and domain-specific parallel data to train robust models. Unfortunately, the existing Chinese–Braille parallel data is insufficient to achieve satisfactory results. To address this challenge, this paper puts forward a groundbreaking approach that integrates pre-training models into the Chinese Braille translation task. This represents the first-ever application of such technology in this context and it is different from traditional pre-training methods. While previous pre-training method of natural language processing mainly utilized raw text data, we have identified its limitations in improving Chinese–Braille translation. Therefore, we have proposed three novel forms of pre-training datasets, instead of relying solely on raw text data. By utilizing the Transformer model, our approach achieves the highest BLEU score of 94.53 on a 10k parallel corpus, presenting a new direction for Chinese–Braille translation research. Furthermore, we introduce a new form of data that enables Chinese–Braille translation solely using the encoder framework. Leveraging the MacBERT model, this approach achieves a BLEU score of 98.87 on the test set and demonstrates an inference speed 54 times faster than the Transformer model. These findings have significant implications for the field of Chinese–Braille translation, providing insights for future research endeavors.