before_sent:  In this work, we propose to encourage the decoder to take the full advantage of the multi-level source representations for layer-wise cross-view decoding . Concretely, different views of the source sequences are presented to different decoder layers and multiple strategies are explored to route the source representations. In particular, the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on the neural machine translation task, surpassing the previous state-of-the-art architecture on three benchmark datasets .
before_sent_with_intent: <meaning-changed>  In this work, we propose to encourage the decoder to take the full advantage of the multi-level source representations for layer-wise cross-view decoding . Concretely, different views of the source sequences are presented to different decoder layers and multiple strategies are explored to route the source representations. In particular, the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on the neural machine translation task, surpassing the previous state-of-the-art architecture on three benchmark datasets .
after_sent:  In this work, we propose to encourage the decoder to take the full advantage of the multi-level source representations for layer-wise cross-view decoding , where for each decoder layer, together with the representations from the last encoder layer, which serve as a global view, those from other encoder layers are supplemented for a stereoscopic view of the source sequences. Systematic experiments show that we successfully address the hierarchy bypassing problem and substantially improve the performance of sequence-to-sequence learning with deep representations on diverse tasks .