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

Andrew Yates, Siddhant Arora, Xinyu Zhang, Wei Yang, Kevin Martin Jose, Jimmy Lin

Abstract: 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.