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 …

In recent years, deep neural networks have led to exciting breakthroughs in speech
recognition, computer vision, and natural language processing (NLP) tasks. However, there …

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 …

Recent years have seen a dramatic growth of natural language text data, including web
pages, news articles, scientific literature, emails, enterprise documents, and social media …

Term weighting schemes are central to the study of information retrieval systems. This article
proposes a novel TF-IDF term weighting scheme that employs two different within document …

In this paper, we reveal a common deficiency of the current retrieval models: the component
of term frequency (TF) normalization by document length is not lower-bounded properly; as …

Explainable information retrieval is an emerging research area aiming to make transparent
and trustworthy information retrieval systems. Given the increasing use of complex machine …

Pretrained contextualized language models such as BERT and T5 have established a new
state-of-the-art for ad-hoc search. However, it is not yet well understood why these methods …

The zero-shot effectiveness of neural retrieval models is often evaluated on the BEIR
benchmark---a combination of different IR evaluation datasets. Interestingly, previous …

Recently, neural networks have been successfully employed to improve upon state-of-the-
art effectiveness in ad-hoc retrieval tasks via machine-learned ranking functions. While …