Utilizing Axiomatic Perturbations to Guide Neural Ranking Models
Zitong Cheng, Hui Min Fang · 2020
Axiomatic approaches aim to utilize reasonable retrieval constraints to guide the search for optimal retrieval models. Existing studies have shown the effectiveness of axiomatic approaches in improving the performance through either the derivation of new basic retrieval models or modifications of existing ones. Recently, neural network models have attracted more attention in the research community. Since these models are learned from training data, it would be interesting to study how to utilize the axiomatic approaches to guide the training process so that the learned models can satisfy retrieval constraints and achieve better retrieval performance.