  Tibetan is a low-resource language. In order to alleviate the shortage of
parallel corpus between Tibetan and Chinese, this paper uses two monolingual
corpora and a small number of seed dictionaries to learn the semi-supervised
method with seed dictionaries and self-supervised adversarial training method
through the similarity calculation of word clusters in different embedded
spaces and puts forward an improved self-supervised adversarial learning method
of Tibetan and Chinese monolingual data alignment only. The experimental
results are as follows. First, the experimental results of Tibetan syllables
Chinese characters are not good, which reflects the weak semantic correlation
between Tibetan syllables and Chinese characters; second, the seed dictionary
of semi-supervised method made before 10 predicted word accuracy of 66.5
(Tibetan - Chinese) and 74.8 (Chinese - Tibetan) results, to improve the
self-supervision methods in both language directions have reached 53.5
accuracy.
