Capreolus: A Toolkit for End-to-End Neural Ad Hoc Retrieval

By Andrew Yates, Siddhant Arora, Xinyu Crystina Zhang, Wei Yang, Kevin Martin Jose, Jimmy J. Lin. 2020

27 citations recorded by the source corpus.

We present Capreolus, a toolkit designed to facilitate end-to-end it ad hoc retrieval experiments with neural networks by providing implementations of prominent neural ranking models within a common framework. Our toolkit adopts a standard reranking architecture via tight integration with the Anserini toolkit for candidate document generation using standard bag-of-words approaches. Using Capreolus, we are able to reproduce Yang et al.'s recent SIGIR 2019 finding that, in a reranking scenario on the test collection from the TREC 2004 Robust Track, many neural retrieval models do not significantly outperform a strong query expansion baseline. Furthermore, we find that this holds true for five additional models implemented in Capreolus. We describe the architecture and design of our toolkit, which includes a Web interface to facilitate comparisons between rankings returned by different models.

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Citations in the corpus

Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations (2021, methodology): “This separation has made it easier for other neural IR toolkits to build on Pyserini, for example, the Caprelous toolkit [29, 30].”
Pyserini: A Python Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations (2021, background): “Today, there is a vibrant ecosystem of search engines built by academic research groups such as Terrier [21] and PISA [24], common infrastructure such as ir_datasets [20], and neural ranking libraries as Capreolus [40, 41], OpenNIR [19], and OpenMatch [36].”