before_sent: In sequence-to-sequence learning, the attention mechanism has been a great success in bridging the information between the encoderand the decoder. However, it is often overlooked that the decoder obtains only a single view of the source sequences, i.e., the representations generated by the last encoder layer .
before_sent_with_intent: <meaning-changed> In sequence-to-sequence learning, the attention mechanism has been a great success in bridging the information between the encoderand the decoder. However, it is often overlooked that the decoder obtains only a single view of the source sequences, i.e., the representations generated by the last encoder layer .
after_sent: In sequence-to-sequence learning, the decoder relies on the attention mechanism to efficiently extract information from the encoder. While it is common practice to draw information from only the last encoder layer, recent work has proposed to use representations from different encoder layers for diversified levels of information. Nonetheless, the decoder still obtains only a single view of the source sequences, i.e., the representations generated by the last encoder layer .