There has been relatively little attention to incorporating linguistic prior to neural machine translation. Much of the previous work was further constrained to considering linguistic prior on the source side. In this paper, we propose a hybrid model, called NMT+RG, that learns to parse and translate by combining the recurrent neural network grammar into the attentionbased neural machine translation. Our approach encourages the neural machine translation model to incorporate linguistic prior during training, and lets it translate on its own afterward. Extensive experiments with four language pairs show the effectiveness of the proposed NMT+RG.
To more closely combine the NMT with syntax tree, REF proposed a hybrid model that learns to parse and translate by combining the recurrent neural network grammar into the attention-based NMT, and thus encouraged the NMT model to incorporate linguistic prior during training, and lets it translate on its own afterward.
Learning to Parse and Translate Improves Neural Machine Translation
ArXiv
ArXiv
Computer Science