Learning Semantic Representations for the Phrase Translation Model

Understand

This paper presents a novel semantic-based phrase translation model.

• A pair of source and target phrases are projected into continuous-valued vector representations in a low-dimensional latent semantic space, where their translation score is computed by the distance between the pair in this new space.
• The projection is performed by a multi-layer neural network whose weights are learned on parallel training data.
• The learning is aimed to directly optimize the quality of end-to-end machine translation results.