Model agnostic interpretability of rankers via intent modelling
Jaspreet Singh, Avishek Anand
Abstract
A key problem in information retrieval is understanding the latent intention of a user's under-specified query. Retrieval models that are able to correctly uncover the query intent often perform well on the document ranking task. In this paper we study the problem of interpretability for text based ranking models by trying to unearth the query intent as understood by complex retrieval models.
Topics & Concepts
InterpretabilityComputer scienceRanking (information retrieval)Information retrievalKey (lock)Task (project management)Query expansionArtificial intelligenceMachine learningEconomicsComputer securityManagementBayesian Modeling and Causal InferenceInformation Retrieval and Search BehaviorTopic Modeling