Research on Tibetan-Chinese Machine Translation Based on Multi-Strategy Processing

This article takes the low-resource nature of Tibetan-Chinese machine translation as the research object, acquires training data through a variety of strategies, and explores the problem of domain adaptability in Tibetan-Chinese materials and the problem of multi-granularity segmentation. Researched the Tibetan-Chinese machine translation method based on Transformer attention mechanism, studied the Tibetan-Chinese machine translation method with different segmentation granularity applied to both ends of encoder-decoder, evaluated multiple granular segmentation, corpus fusion of different fields and different types. The effect of corpus fusion is the experimental result with the highest BLEU score of 44.9 points.