Pyserini is a Python toolkit for reproducible information retrieval research with sparse and
dense representations. It aims to provide effective, reproducible, and easy-to-use first-stage …

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 …

Managing the data for Information Retrieval (IR) experiments can be challenging. Dataset
documentation is scattered across the Internet and once one obtains a copy of the data …

Pyserini is an easy-to-use Python toolkit that supports replicable IR research by providing
effective first-stage retrieval in a multi-stage ranking architecture. Our toolkit is self-contained …

While much recent work has demonstrated that hard negative mining can be used to train
better bi-encoder models, few have considered it in the context of cross-encoders, which are …

Traditionally, sparse retrieval systems relied on lexical representations to retrieve
documents, such as BM25, dominated information retrieval tasks. With the onset of pre …

While BERT has been shown to be effective for passage retrieval, its maximum input length
limitation poses a challenge when applying the model to document retrieval. In this work, we …

Axiomatic approaches to information retrieval have played a key role in determining basic
constraints that characterize good retrieval models. Beyond their importance in retrieval …

Researchers have proposed simple yet effective techniques for the retrieval problem based
on using BERT as a relevance classifier to rerank initial candidates from keyword search. In …