is paper proposes K-NRM, a kernel based neural model for document ranking. Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling technique that uses kernels to extract multi-level so match features, and a learning-to-rank layer that combines those features into the nal ranking score. e whole model is trained end-to-end. e ranking layer learns desired feature pa erns from the pairwise ranking loss. e kernels transfer the feature pa erns into so -match targets at each similarity level and enforce them on the translation matrix. e word embeddings are tuned accordingly so that they can produce the desired so matches. Experiments on a commercial search engine's query log demonstrate the improvements of K-NRM over prior feature-based and neural-based states-of-the-art, and explain the source of K-NRM's advantage: Its kernel-guided embedding encodes a similarity metric tailored for matching query words to document words, and provides e ective multi-level so matches.
K-NRM REF is an interaction-based model that uses kernel pooling to summarize word-word interactions.
End-to-End Neural Ad-hoc Ranking with Kernel Pooling
SIGIR '17
Computer Science