With the ever increasing size of the web, relevant information extraction on the Internet with
a query formed by a few keywords has become a big challenge. Query Expansion (QE) …

Multi-stage ranking pipelines have been a practical solution in modern search systems,
where the first-stage retrieval is to return a subset of candidate documents and latter stages …

Conducting text retrieval in a dense learned representation space has many intriguing
advantages over sparse retrieval. Yet the effectiveness of dense retrieval (DR) often requires …

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 …

This paper introduces a simple yet effective query expansion approach, denoted as
query2doc, to improve both sparse and dense retrieval systems. The proposed method first …

Classical information retrieval systems such as BM25 rely on exact lexical match and carry
out search efficiently with inverted list index. Recent neural IR models shifts towards soft …

Users often fail to formulate their complex information needs in a single query. As a
consequence, they may need to scan multiple result pages or reformulate their queries …

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 …

Software toolkits play an essential role in information retrieval research. Most open-source
toolkits developed by academics are designed to facilitate the evaluation of retrieval models …