论文标题
非线性多个字段互动神经文档排名
Non-Linear Multiple Field Interactions Neural Document Ranking
论文作者
论文摘要
排名任务通常基于页面主体的文本以及页面上用户的操作（点击）。还可以利用其他元素来更好地将排名体验的上下文化（例如，在其他字段中的文本，用户进行的查询，图像等）。我们介绍了两个单独的数据集中多个字段排名的现场相互作用的第一个深入分析之一。尽管某些作品利用了完整的文档结构，但一些方面仍未开发。在这项工作中，我们基于先前的分析，以显示查询场相互作用，非线性场相互作用以及基础神经模型的体系结构影响性能。
Ranking tasks are usually based on the text of the main body of the page and the actions (clicks) of users on the page. There are other elements that could be leveraged to better contextualise the ranking experience (e.g. text in other fields, query made by the user, images, etc). We present one of the first in-depth analyses of field interaction for multiple field ranking in two separate datasets. While some works have taken advantage of full document structure, some aspects remain unexplored. In this work we build on previous analyses to show how query-field interactions, non-linear field interactions, and the architecture of the underlying neural model affect performance.
