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 |
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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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