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Neural sequence-to-sequence networks with attention have achieved remarkableperformance for machine translation. One of the reasons for their effectivenessis their ability to capture relevant source-side contextual information at eachtime-step prediction through an attention mechanism. However, the target-sidecontext is solely based on the sequence model which, in practice, is prone to arecency bias and lacks the ability to capture effectively non-sequentialdependencies among words. To address this limitation, we propose atarget-side-attentive residual recurrent network for decoding, where attentionover previous words contributes directly to the prediction of the next word.The residual learning facilitates the flow of information from the distant pastand is able to emphasize any of the previously translated words, hence it gainsaccess to a wider context. The proposed model outperforms a neural MT baselineas well as a memory and self-attention network on three language pairs. Theanalysis of the attention learned by the decoder confirms that it emphasizes awider context, and that it captures syntactic-like structures.

Self-Attentive Residual Decoder for Neural Machine Translation