A maturing area of research in deep learning is the development of
architectures that can learn explicit representations of relational features.
In this paper, we focus on the problem of learning representations of
hierarchical relations, proposing an architectural framework we call
"relational convolutional networks". Given a sequence of objects, a
"multi-dimensional inner product relation" module produces a relation tensor
describing all pairwise relations. A "relational convolution" layer then
transforms the relation tensor into a sequence of new objects, each describing
the relations within some group of objects at the previous layer. Graphlet
filters, analogous to filters in convolutional neural networks, represent a
template of relations against which the relation tensor is compared at each
grouping. Repeating this yields representations of higher-order, hierarchical
relations. We present the motivation and details of the architecture, together
with a set of experiments to demonstrate how relational convolutional networks
can provide an effective framework for modeling relational tasks that have
hierarchical structure.

深度学习中一个日益成熟的研究领域是开发能够学习关系特征的显式表示的架构。本文提出了一个叫做 “关系卷积网络” 的架构框架，重点研究了学习分层关系表示的问题，并通过一系列实验展示了如何利用关系卷积网络提供有效的建模方法来处理具有层次结构的关系任务。

关系卷积网络：一种学习层次关系表示的框架

Relational Convolutional Networks: A framework for learning  representations of hierarchical relations

This work aims to tackle the challenging heterogeneous graph encoding problem
in the text-to-SQL task. Previous methods are typically node-centric and merely
utilize different weight matrices to parameterize edge types, which 1) ignore
the rich semantics embedded in the topological structure of edges, and 2) fail
to distinguish local and non-local relations for each node. To this end, we
propose a Line Graph Enhanced Text-to-SQL (LGESQL) model to mine the underlying
relational features without constructing meta-paths. By virtue of the line
graph, messages propagate more efficiently through not only connections between
nodes, but also the topology of directed edges. Furthermore, both local and
non-local relations are integrated distinctively during the graph iteration. We
also design an auxiliary task called graph pruning to improve the
discriminative capability of the encoder. Our framework achieves
state-of-the-art results (62.8% with Glove, 72.0% with Electra) on the
cross-domain text-to-SQL benchmark Spider at the time of writing.

本研究提出了一种针对 text-to-SQL 任务中具有挑战性的异构图编码问题的 LGESQL 模型，通过线图增强策略挖掘底层的关系特征，同时设计了一个名为图修剪的辅助任务，提高编码器的判别能力，在跨领域的文本到 SQL 测试 Spider 上取得了最先进的结果。