DAM:  performs matching under a representation-matching-aggregation framework, and represents a context and a response with stacked self-attention and crossattention.
one model may guide the other how to pick high quality training examples and circumvent noise; second, since the peer models are optimized with different training sub-batches, knowledge from one sub-batch could be supplementary to the other through exchange of learning protocols; third, the two peer models may have different decision boundaries, and thus are good at recognizing different patterns in data.