End-to-End Neural Ad-hoc Ranking with Kernel Pooling

Understand

This 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 soft match features, and a learning-to-rank layer that combines those features into the final ranking score.
• The whole model is trained end-to-end.
• The ranking layer learns desired feature patterns from the pairwise ranking loss.