Optimization Algorithms for Tibetan-Chinese Neural Machine Translation

Abstract

Tibetan-Chinese neural machine translation (NMT) is facing serious resource scarcity problem. This paper compares the application effect of multiple neural network optimization algorithms under the condition of resource scarcity, and proposes an optimization method which is suitable for resource scarcity languages. Then, by which it improving the performance of the Tibetan-Chinese NMT. Experimental results show that when choosing a suitable optimization algorithm, the Tibetan-Chinese NMT can still exceed the traditional statistical machine translation (SMT) and achieve better translation performance.