Fact verification tasks aim to identify the integrity of textual contents
according to the truthful corpus. Existing fact verification models usually
build a fully connected reasoning graph, which regards claim-evidence pairs as
nodes and connects them with edges. They employ the graph to propagate the
semantics of the nodes. Nevertheless, the noisy nodes usually propagate their
semantics via the edges of the reasoning graph, which misleads the semantic
representations of other nodes and amplifies the noise signals. To mitigate the
propagation of noisy semantic information, we introduce a Confidential Graph
Attention Network (CO-GAT), which proposes a node masking mechanism for
modeling the nodes. Specifically, CO-GAT calculates the node confidence score
by estimating the relevance between the claim and evidence pieces. Then, the
node masking mechanism uses the node confidence scores to control the noise
information flow from the vanilla node to the other graph nodes. CO-GAT
achieves a 73.59% FEVER score on the FEVER dataset and shows the generalization
ability by broadening the effectiveness to the science-specific domain.

Fact verification tasks using a Confidential Graph Attention Network (CO-GAT) with a node masking mechanism achieve a 73.59% FEVER score on the FEVER dataset and show generalization ability to science-specific domains.

多证据基于机密图神经网络的事实验证

Multi-Evidence based Fact Verification via A Confidential Graph Neural  Network

In this paper we show that simple noise regularisation can be an effective
way to address GNN oversmoothing. First we argue that regularisers addressing
oversmoothing should both penalise node latent similarity and encourage
meaningful node representations. From this observation we derive "Noisy Nodes",
a simple technique in which we corrupt the input graph with noise, and add a
noise correcting node-level loss. The diverse node level loss encourages latent
node diversity, and the denoising objective encourages graph manifold learning.
Our regulariser applies well-studied methods in simple, straightforward ways
which allow even generic architectures to overcome oversmoothing and achieve
state of the art results on quantum chemistry tasks, and improve results
significantly on Open Graph Benchmark (OGB) datasets. Our results suggest Noisy
Nodes can serve as a complementary building block in the GNN toolkit.

本研究表明，简单的噪声正则化可以是解决 GNN 过度平滑的有效方式。通过在输入图形上添加噪声并添加噪声校正的节点级损失，我们提出了 “噪声节点” 技术。该技术可促进图形多流形学习，并在量子化学任务上取得最新成果及显著提高 Open Graph Benchmark（OGB）数据集的结果，从而成为 GNN 工具包中的有效建模模块。