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Consistency and Variation in Kernel Neural Ranking Model
This paper studies the consistency of the kernel-based neural ranking model
K-NRM, a recent state-of-the-art neural IR model, which is important for
reproducible research and deployment in the industry. We find that K-NRM has
low variance on relevance-based metrics across experimental trials. In spite of
this low variance in overall performance, different trials produce different
document rankings for individual queries. The main source of variance in our
experiments was found to be different latent matching patterns captured by
K-NRM. In the IR-customized word embeddings learned by K-NRM, the
query-document word pairs follow two different matching patterns that are
equally effective, but align word pairs differently in the embedding space. The
different latent matching patterns enable a simple yet effective approach to
construct ensemble rankers, which improve K-NRM's effectiveness and
generalization abilities.Comment: 4 pages, 4 figures, 2 table
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