Zero-shot entity retrieval, aiming to link mentions to candidate entities
under the zero-shot setting, is vital for many tasks in Natural Language
Processing. Most existing methods represent mentions/entities via the sentence
embeddings of corresponding context from the Pre-trained Language Model.
However, we argue that such coarse-grained sentence embeddings can not fully
model the mentions/entities, especially when the attention scores towards
mentions/entities are relatively low. In this work, we propose GER, a
\textbf{G}raph enhanced \textbf{E}ntity \textbf{R}etrieval framework, to
capture more fine-grained information as complementary to sentence embeddings.
We extract the knowledge units from the corresponding context and then
construct a mention/entity centralized graph. Hence, we can learn the
fine-grained information about mention/entity by aggregating information from
these knowledge units. To avoid the graph information bottleneck for the
central mention/entity node, we construct a hierarchical graph and design a
novel Hierarchical Graph Attention Network~(HGAN). Experimental results on
popular benchmarks demonstrate that our proposed GER framework performs better
than previous state-of-the-art models. The code has been available at
this https URL.

提出了 GER：一个图增强型架构以获取零样本实体检索任务中更加精细的信息，并使用分层图注意力网络避免中心实体节点的图信息瓶颈，实验证明 GER 模型在常见基准测试中优于现有最先进模型。

基于知识感知的分层图模型进行细粒度信息建模，用于零样本实体检索

Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval

The viral spread of fake news has caused great social harm, making fake news
detection an urgent task. Current fake news detection methods rely heavily on
text information by learning the extracted news content or writing style of
internal knowledge. However, deliberate rumors can mask writing style,
bypassing language models and invalidating simple text-based models. In fact,
news articles and other related components (such as news creators and news
topics) can be modeled as a heterogeneous information network (HIN for short).
In this paper, we propose a novel fake news detection framework, namely
Hierarchical Graph Attention Network(HGAT), which uses a novel hierarchical
attention mechanism to perform node representation learning in HIN, and then
detects fake news by classifying news article nodes. Experiments on two
real-world fake news datasets show that HGAT can outperform text-based models
and other network-based models. In addition, the experiment proved the
expandability and generalizability of our for graph representation learning and
other node classification related applications in heterogeneous graphs.

该研究提出了一种名为 HGAT (Hierarchical Graph Attention Network) 的框架，该框架利用一种新颖的分层注意机制，在异构信息网络中进行节点表示学习，通过对新闻文章节点进行分类来检测假新闻。实验结果表明，HGAT 可以胜过基于文本和其他基于网络结构的模型，并且该框架还具有与其他节点分类相关应用程序的图形表示学习的扩展性和通用性。

基于层次图注意力的新闻异构信息网络假新闻检测

Fake News Detection on News-Oriented Heterogeneous Information Networks  through Hierarchical Graph Attention

Most previous studies on multi-agent reinforcement learning focus on deriving
decentralized and cooperative policies to maximize a common reward and rarely
consider the transferability of trained policies to new tasks. This prevents
such policies from being applied to more complex multi-agent tasks. To resolve
these limitations, we propose a model that conducts both representation
learning for multiple agents using hierarchical graph attention network and
policy learning using multi-agent actor-critic. The hierarchical graph
attention network is specially designed to model the hierarchical relationships
among multiple agents that either cooperate or compete with each other to
derive more advanced strategic policies. Two attention networks, the
inter-agent and inter-group attention layers, are used to effectively model
individual and group level interactions, respectively. The two attention
networks have been proven to facilitate the transfer of learned policies to new
tasks with different agent compositions and allow one to interpret the learned
strategies. Empirically, we demonstrate that the proposed model outperforms
existing methods in several mixed cooperative and competitive tasks.

本文提出了一种基于分层图注意力网络和多智能体 actor-critic 的模型，可以进行多智能体表示学习和多智能体策略学习，通过两种特殊设计的图注意力网络促进多智能体之间的协作和竞争，实现在新任务中的策略迁移并优于其他现有方法。