This paper proposes a novel approach towards better interpretability of a trained text-based
ranking model in a post-hoc manner. A popular approach for post-hoc interpretability text …

We present an explainable document search system (ExDocS), based on a re-ranking
approach, that uses textual and visual explanations to explain document rankings to non …

Neural document ranking models perform impressively well due to superior language
understanding gained from pre-training tasks. However, due to their complexity and large …

Information retrieval (IR) models often employ complex variations in term weights to compute
an aggregated similarity score of a query-document pair. Treating IR models as black-boxes …

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 …

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 …

Retrieval models in information retrieval are used to rank documents for typically under-
specified queries. Today machine learning is used to learn retrieval models from click logs …

Interpretability of ranking models is a crucial yet relatively under-examined research area.
Recent progress on this area largely focuses on generating post-hoc explanations for …

As the heart of a search engine, the ranking system plays a crucial role in satisfying users'
information demands. More recently, neural rankers fine-tuned from pre-trained language …

Many ranking models have been proposed in information retrieval, and recently machine
learning techniques have also been applied to ranking model construction. Most of the …