Important matching information between segment pairs across multi-turn context and candidate response is then extracted via convolution with max-pooling operations, and further fused into one matching score via a single-layer perceptron, representing the matching degree between the response candidate and the whole context.
we use a shared component, the Attentive Module, to implement both selfattention in representation and cross-attention in matching.