Improving Neural Machine Translation by Achieving Knowledge Transfer with Sentence Alignment Learning
Neural Machine Translation (NMT) optimized by Maximum Likelihood Estimation (MLE) lacks the guarantee of translation adequacy. To alleviate this problem, we propose an NMT approach that heightens the adequacy in machine translation by transferring the semantic knowledge learned from bilingual sentence alignment. Specifically, we first design a discriminator that learns to estimate sentence aligning score over translation candidates, and then the learned semantic knowledge is transfered to the NMT model under an adversarial learning framework. We also propose a gated self attention based encoder for sentence embedding. Furthermore, an N pair training loss is introduced in our framework to aid the discriminator in better capturing lexical evidence in translation candidates. Experimental results show that our proposed method outperforms baseline NMT models on Chinese to English and English to German translation tasks. Further analysis also indicates the detailed semantic knowledge transfered from the discriminator to the NMT model.
CoNLL