Neural Network Joint Modeling Via Context-Dependent Projection

Neural network joint modeling (NNJM) has produced huge improvement in machine translation performance. As in standard neural network language modeling, a context-independent linear projection is applied to project a sparse input vector into a continuous representation at each word position. Because neighboring words are dependent on each other, context-independent projection may not be optimal. We propose a context-dependent linear projection approach which considers neighboring words. Experimental results showed that the proposed approach further improves NNJM by 0.5 BLEU for English-Iraqi Arabic translation in N-best rescoring. Compared to a baseline using hierarchical phrases and sparse features, NNJM with our proposed approach has achieved a 2 BLEU improvement.