This paper provides an extensive and thorough overview of the models and techniques
utilized in the first and second stages of the typical information retrieval processing chain …

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …

Ranking models lie at the heart of research on information retrieval (IR). During the past
decades, different techniques have been proposed for constructing ranking models, from …

Pre-trained transformer models, such as BERT and T5, have shown to be highly effective at
ad hoc passage and document ranking. Due to the inherent sequence length limits of these …

Recently pre-trained language representation models such as BERT have shown great
success when fine-tuned on downstream tasks including information retrieval (IR). However …

Adversarial ranking attacks have gained increasing attention due to their success in probing
vulnerabilities, and, hence, enhancing the robustness, of neural ranking models …

Neural networks, particularly Transformer-based architectures, have achieved significant
performance improvements on several retrieval benchmarks. When the items being …

Pre-training and fine-tuning have achieved remarkable success in many downstream
natural language processing (NLP) tasks. Recently, pre-training methods tailored for …

Matching is the key problem in both search and recommendation, that is to measure the
relevance of a document to a query or the interest of a user on an item. Previously, machine …

Search engines operate under a strict time constraint as a fast response is paramount to
user satisfaction. Thus, neural reranking models have a limited time-budget to re-rank …