Future-Prediction-Based Model for Neural Machine Translation
This addresses translation quality issues for users of NMT systems, but appears incremental as it builds on existing methods with a specific enhancement.
The paper tackles the problem of incomplete translations in Neural Machine Translation by proposing a model that predicts future text length and words during decoding, which significantly outperforms baseline models.
We propose a novel model for Neural Machine Translation (NMT). Different from the conventional method, our model can predict the future text length and words at each decoding time step so that the generation can be helped with the information from the future prediction. With such information, the model does not stop generation without having translated enough content. Experimental results demonstrate that our model can significantly outperform the baseline models. Besides, our analysis reflects that our model is effective in the prediction of the length and words of the untranslated content.