In particular, we follow the intuition that as CNNs learn the embeddings in a classification setting they should be used in the same way for building the reranking kernel, i.e., we need to use the embeddings in a pointwise reranking fashion.
we propose a hybrid preference-pointwise kernel, which consists in (i) a standard reranking kernel based on CTKs applied to the Q/AP structural representations; and (ii) a classification kernel based on the embeddings learned by neural networks.