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 …

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

Assessing relevance between a query and a document is challenging in ad-hoc retrieval
due to its diverse patterns, ie, a document could be relevant to a query as a whole or …

An emerging recipe for achieving state-of-the-art effectiveness in neural document re-
ranking involves utilizing large pre-trained language models-eg, BERT-to evaluate all …

The ad hoc retrieval task is to find documents in a corpus that are relevant to a query.
Inspired by the PageRank and HITS (hubs and authorities) algorithms for Web search, we …

Many existing retrieval approaches do not take into account the content quality of the
retrieved documents, although link-based measures such as PageRank are commonly used …

We focus on the task of Frequently Asked Questions (FAQ) retrieval. A given user query can
be matched against the questions and/or the answers in the FAQ. We present a fully …

Learning sparse representations using pretrained language models enhances the
monolingual ranking effectiveness. Such representations are sparse vectors in the …

Many of the recent, and more effective, retrieval models have incorporated dependencies
between the terms in the query. In this paper, we advance this query representation one step …

With the overwhelming number of reports on similar events originating from different sources
on the web, it is often hard, using existing web search paradigms, to find the original source …