基于PyTerrier的信息检索陈述性实验
Declarative Experimentation in Information Retrieval using PyTerrier
摘要：
深度机器学习平台的出现，如Tensorflow和Pytorch，是用Python等表达性高级语言开发的，允许深度神经网络架构的表达性更强。我们认为，这样一个强大的形式主义是失踪的信息检索（IR），并提出了一个框架，称为PyTerrier，允许先进的检索管道表示，并评估，在声明的方式接近他们的概念设计。与前面提到的将深度学习实验编译为基本GPU操作的框架一样，我们的框架将IR平台作为后端，以执行和评估检索管道。此外，我们可以自动优化检索管道，以提高其效率，以适应特定的IR平台后端。我们的实验，进行TREC鲁棒和CNOWWeb09测试集合，证明了这些优化检索管道涉及的Anserini和梗IR平台的效率优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
