利用公理扰动指导神经排序模型

Zitong Cheng, Hui Fang
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引用次数: 2

摘要

公理方法旨在利用合理的检索约束来指导对最优检索模型的搜索。已有的研究表明,公理化方法可以通过推导新的基本检索模型或对现有检索模型进行修改来提高检索性能。近年来,神经网络模型越来越受到研究界的关注。由于这些模型是从训练数据中学习而来的,因此如何利用公理方法来指导训练过程,使学习到的模型能够满足检索约束,获得更好的检索性能,是一个值得研究的问题。在本文中,我们提出利用公理摄动来构造神经排序模型的训练数据集。扰动数据集的构造方式可以放大任何合理的检索模型应该满足的理想属性。因此,期望从扰动数据集中学习到的模型能够满足更多的检索约束,从而获得更好的检索性能。实验结果表明,从扰动数据集学习到的模型确实比从原始数据集学习到的模型性能更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilizing Axiomatic Perturbations to Guide Neural Ranking Models
Axiomatic approaches aim to utilize reasonable retrieval constraints to guide the search for optimal retrieval models. Existing studies have shown the effectiveness of axiomatic approaches in improving the performance through either the derivation of new basic retrieval models or modifications of existing ones. Recently, neural network models have attracted more attention in the research community. Since these models are learned from training data, it would be interesting to study how to utilize the axiomatic approaches to guide the training process so that the learned models can satisfy retrieval constraints and achieve better retrieval performance. In this paper, we propose to utilize axiomatic perturbations to construct training data sets for neural ranking models. The perturbed data sets are constructed in a way to amplify the desirable properties that any reasonable retrieval models should satisfy. As a result, the models learned from the perturbed data sets are expected to satisfy more retrieval constraints and lead to better retrieval performance. Experiment results show that the models learned from the perturbed data sets indeed perform better than those learned from the original data sets.
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