Tibetan-Chinese Neural Machine Translation Combining Attention Mechanism
Tao Jiang, Hao Sun, Yu Gang Dai, Ding Liu · Journal of Physics Conference Series · 2020
Abstract Neural Machine Translation (NMT) has developed rapidly in recent years, and translation effects in most languages have surpassed statistical machine translation methods. The Seq2Seq framework has brought great advantages to machine translation, but the model still has great limitations in the ability to capture long-distance information. Recurrent neural networks (RNN) and LSTM networks are proposed to improve this problem, but the effect is not obvious. However, the proposal and application of the attention mechanism effectively compensated for this defect. This paper uses the attention mechanism as the basis to construct the encoder-decoder framework. This method analyzes the mechanism and principle of the attention model to implement the Tibetan-Chinese neural machine translation system. Compared with the previous neural network translation models, the results show that the translation model that incorporates the attention mechanism can obtain better translation results.