A novel approach to neural machine translation

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Introduced a novel convolutional neural network (CNN) approach for neural machine translation, achieving state-of-the-art accuracy at nine times the speed of recurrent neural systems. The approach utilizes multi-hop attention and gating mechanisms for improved translation quality and efficiency. The fairseq toolkit and trained systems were open-sourced.