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

## TL;DR

This paper introduces K-NRM, a neural ranking model that uses kernel pooling and word embeddings to improve document ranking by capturing multi-level soft matches, trained end-to-end for better performance.

## Contribution

The paper presents a novel kernel pooling technique and a translation matrix for neural ad-hoc ranking, enabling end-to-end training and improved matching accuracy.

## Key findings

- K-NRM outperforms prior state-of-the-art models on commercial search logs.
- Kernel-guided embeddings encode a similarity metric tailored for matching.
- Multi-level soft matches enhance ranking effectiveness.

## Abstract

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. The kernels transfer the feature patterns into soft-match targets at each similarity level and enforce them on the translation matrix. The word embeddings are tuned accordingly so that they can produce the desired soft 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 effective multi-level soft matches.