For seq2BF without content introducing, we obtain low scores, showing that artificially splitting a sequence into two parts itself is not a fancy way of modeling natural language sentences.
given a keyword predicted by PMI statistics, the backward and forward sequence generation can significantly improve the dialogue system in comparison with pure seq2seq.