before_sent:  In this work, we explore to reuse the representations from different encoder layers for layer-wise cross-view decoding , that is, different views of the source sequences are presented to different decoder layers . We investigate multiple , representative strategies for cross-view coding, of which the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on  neural machine translation task . Especially, GCA surpasses the previous state-of-the-art architecture on three machine translation datasets.
before_sent_with_intent: <clarity>  In this work, we explore to reuse the representations from different encoder layers for layer-wise cross-view decoding , that is, different views of the source sequences are presented to different decoder layers . We investigate multiple , representative strategies for cross-view coding, of which the granularity consistent attention (GCA) strategy proves the most efficient and effective in the experiments on  neural machine translation task . Especially, GCA surpasses the previous state-of-the-art architecture on three machine translation datasets.
after_sent:  In this work, we explore to reuse the representations from different encoder layers for layer-wise cross-view decoding , that is, 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 neural machine translation task . Especially, GCA surpasses the previous state-of-the-art architecture on three machine translation datasets.