Learning to Parse and Translate Improves Neural Machine Translation
This work addresses the lack of linguistic prior integration in neural machine translation, which is an incremental improvement for the field.
The paper tackles the problem of incorporating linguistic prior into neural machine translation by proposing a hybrid model that learns to parse and translate, showing effectiveness across four language pairs.
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+RNNG, that learns to parse and translate by combining the recurrent neural network grammar into the attention-based 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+RNNG.