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 …

Neural models have demonstrated remarkable performance across diverse ranking tasks.
However, the processes and internal mechanisms along which they determine relevance …

Learning-to-rank (LTR) is a class of supervised learning techniques that apply to ranking
problems dealing with a large number of features. The popularity and widespread …

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 …

Learning to rank has been intensively studied and widely applied in information retrieval.
Typically, a global ranking function is learned from a set of labeled data, which can achieve …

Contextual models like BERT are highly effective in numerous text-ranking tasks. However, it
is still unclear as to whether contextual models understand well-established notions of …

Several neural networks have been developed for end-to-end training of information
retrieval models. These networks differ in many aspects including architecture, training data …

Language models contain ranking-based knowledge and are powerful solvers of in-context
ranking tasks. For instance, they may have parametric knowledge about the ordering of …

In traditional retrieval environments, a ranked list of candidate documents is produced
without regard to the number of documents. With the rise in interactive IR as well as …

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