  Axiomatic information retrieval (IR) seeks a set of principle properties
desirable in IR models. These properties when formally expressed provide
guidance in the search for better relevance estimation functions. Neural
ranking models typically contain a large number of parameters. The training of
these models involve a search for appropriate parameter values based on large
quantities of labeled examples. Intuitively, axioms that can guide the search
for better traditional IR models should also help in better parameter
estimation for machine learning based rankers. This work explores the use of IR
axioms to augment the direct supervision from labeled data for training neural
ranking models. We modify the documents in our dataset along the lines of
well-known axioms during training and add a regularization loss based on the
agreement between the ranking model and the axioms on which version of the
document---the original or the perturbed---should be preferred. Our experiments
show that the neural ranking model achieves faster convergence and better
generalization with axiomatic regularization.
