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 …

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 …

Evaluation efforts such as TREC, CLEF, NTCIR and FIRE, alongside public leaderboard
such as MS MARCO, are intended to encourage research and track our progress …

Ship detection by using remote-sensing images based on a synthetic aperture radar (SAR)
plays an important role in managing water transportation and marine safety. However …

Modern transformer-based information retrieval models achieve state-of-the-art performance
across various benchmarks. The self-attention of the transformer models is a powerful …

On a wide range of natural language processing and information retrieval tasks, transformer-
based models, particularly pre-trained language models like BERT, have demonstrated …

Recently transformer-based ranking models have been shown to deliver high relevance for
document search and the relevance-efficiency tradeoff becomes important for fast query …

Long document re-ranking has been a challenging problem for neural re-rankers based on
deep language models like BERT. Early work breaks the documents into short passage-like …

Cross-encoders are effective passage and document re-rankers but less efficient than other
neural or classic retrieval models. A few previous studies have applied windowed self …