Encoding Source Language with Convolutional Neural Network for Machine Translation

Understand

The recently proposed neural network joint model (NNJM) (Devlin et al., 2014) augments the n-gram target language model with a heuristically chosen source context window, achieving state-of-the-art performance in SMT.

• In this paper, we give a more systematic treatment by summarizing the relevant source information through a convolutional architecture guided by the target information.
• With different guiding signals during decoding, our specifically designed convolution+gating architectures can pinpoint the parts of a source sentence that are relevant to predicting a target word, and fuse them with the context of entire source sentence to form a unified representation.
• This representation, together with target language words, are fed to a deep neural network (DNN) to form a stronger NNJM.