论文标题
深QPP：一种基于成对互动的深度学习模型，用于监督查询性能预测
Deep-QPP: A Pairwise Interaction-based Deep Learning Model for Supervised Query Performance Prediction
论文作者
论文摘要
由于最近端到端深层神经模型在排名任务中的成功，我们在这里提出了一种监督的端到端神经方法，以进行查询性能预测（QPP）。与依赖文档分布的各种统计数据的无监督方法相反，我们的方法完全由数据驱动。此外，与弱监督的方法相反，我们的方法也不依赖于不同QPP估计器的输出。特别是，我们的模型利用查询术语和与之检索的顶级文件中的语义相互作用的信息。该模型的体系结构包括2D卷积过滤器的多层层，然后是一个参数的进料层。对标准测试收集的实验表明，我们提出的监督方法优于其他最先进的监督和无监督的方法。
Motivated by the recent success of end-to-end deep neural models for ranking tasks, we present here a supervised end-to-end neural approach for query performance prediction (QPP). In contrast to unsupervised approaches that rely on various statistics of document score distributions, our approach is entirely data-driven. Further, in contrast to weakly supervised approaches, our method also does not rely on the outputs from different QPP estimators. In particular, our model leverages information from the semantic interactions between the terms of a query and those in the top-documents retrieved with it. The architecture of the model comprises multiple layers of 2D convolution filters followed by a feed-forward layer of parameters. Experiments on standard test collections demonstrate that our proposed supervised approach outperforms other state-of-the-art supervised and unsupervised approaches.
