Deep semantic matching aims to discriminate the relationship between documents based on deep neural networks. In recent years, it becomes increasingly popular to organize documents with a graph structure, then leverage both the intrinsic document features and the extrinsic neighbor features to derive discrimination. Most of the existing works mainly care about how to utilize the presented neighbors, whereas limited effort is made to filter appropriate neighbors. We argue that the neighbor features could be highly noisy and partially useful. Thus, a lack of effective neighbor selection will not only incur a great deal of unnecessary computation cost, but also restrict the matching accuracy severely. In this work, we propose a novel framework, Cascaded Deep Semantic Matching (CDSM), for accurate and efficient semantic matching on textual graphs. CDSM is highlighted for its two-stage workflow. In the first stage, a lightweight CNN-based ad-hod neighbor selector is deployed to filter useful neighbors for the matching task with a small computation cost. We design both one-step and multi-step selection methods. In the second stage, a high-capacity graph-based matching network is employed to compute fine-grained relevance scores based on the well-selected neighbors. It is worth noting that CDSM is a generic framework which accommodates most of the mainstream graph-based semantic matching networks. The major challenge is how the selector can learn to discriminate the neighbors usefulness which has no explicit labels. To cope with this problem, we design a weak-supervision strategy for optimization, where we train the graph-based matching network at first and then the ad-hoc neighbor selector is learned on top of the annotations from the matching network.
翻译:深层的语义匹配旨在区分基于深层神经网络的文档之间的关系。 近些年来, 以图表结构来组织文档越来越受欢迎, 然后利用内在文档特性和外部邻居特性来进行区分。 大多数现有作品主要关心如何利用所介绍的邻居, 而只是做出了有限的努力来过滤适当的邻居。 我们争辩说, 邻居特征可能非常吵闹, 部分有用。 因此, 缺乏有效的邻居选择不仅会带来大量不必要的计算成本, 还会严重限制匹配的准确性。 在这项工作中, 我们提议了一个新的框架, 以图形结构为结构, 从而利用内部文档特性和外部邻居特性来进行精确和高效的语义匹配。 大部分现有作品主要工作都关注如何利用光量的CNN( ) 模拟邻居选择器来过滤有用的邻居, 从而用小的计算成本来匹配任务。 因此, 我们设计了一步和多步的选择方法。 在第二个阶段, 一个基于高功能的图形匹配网络, 选择一个基于精细的互联网定义, 以精确的精细的逻辑定义, 定义网络是如何在高层次上进行。