Learning Dense Models of Query Similarity from User Click Logs

The goal of this work is to integrate query similarity metrics
as features into a dense model that can be trained on large amounts of
query log data, in order to rank query rewrites. We propose features
that incorporate various notions of syntactic and semantic similarity in
a generalized edit distance framework. We use the implicit feedback of
user clicks on search results as weak labels in training linear ranking
models on large data sets. We optimize different ranking objectives in a
stochastic gradient descent framework. Our experiments show that a
pairwise SVM ranker trained on multipartite rank levels outperforms
other pairwise and listwise ranking methods under a variety of
evaluation metrics.