Declarative Experimentation in Information Retrieval Using PyTerrier

Macdonald, C. and Tonellotto, N. (2020) Declarative Experimentation in Information Retrieval Using PyTerrier. In: ICTIR 2020: The 6th ACM International Conference on the Theory of Information Retrieval, Stavanger, Norway, 14-18 Sep 2020, pp. 161-168. ISBN 9781450380676 (doi: 10.1145/3409256.3409829)

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Abstract

The advent of deep machine learning platforms such as Tensorflow and Pytorch, developed in expressive high-level languages such as Python, have allowed more expressive representations of deep neural network architectures. We argue that such a powerful formalism is missing in information retrieval (IR), and propose a framework called PyTerrier that allows advanced retrieval pipelines to be expressed, and evaluated, in a declarative manner close to their conceptual design. Like the aforementioned frameworks that compile deep learning experiments into primitive GPU operations, our framework targets IR platforms as backends in order to execute and evaluate retrieval pipelines. Further, we can automatically optimise the retrieval pipelines to increase their efficiency to suite a particular IR platform backend. Our experiments, conducted on TREC Robust and ClueWeb09 test collections, demonstrate the efficiency benefits of these optimisations for retrieval pipelines involving both the Anserini and Terrier IR platforms.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Macdonald, Professor Craig
Authors: Macdonald, C., and Tonellotto, N.
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781450380676
Copyright Holders:Copyright © 2020 Association for Computing Machinery
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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