The widespread of fake news and misinformation in various domains ranging
from politics, economics to public health has posed an urgent need to
automatically fact-check information. A recent trend in fake news detection is
to utilize evidence from external sources. However, existing evidence-aware
fake news detection methods focused on either only word-level attention or
evidence-level attention, which may result in suboptimal performance. In this
paper, we propose a Hierarchical Multi-head Attentive Network to fact-check
textual claims. Our model jointly combines multi-head word-level attention and
multi-head document-level attention, which aid explanation in both word-level
and evidence-level. Experiments on two real-word datasets show that our model
outperforms seven state-of-the-art baselines. Improvements over baselines are
from 6\% to 18\%. Our source code and datasets are released at
\texttt{https://github.com/nguyenvo09/EACL2021}.

本研究提出了一种基于 Hierarchical Multi-head Attentive Network 的虚假新闻检测方法，该方法使用多头词级别和文档级别的注意力实现解释，并在两个实际数据集上优于七种现有方法。

多头注意力层级网络用于证据感知的假新闻检测

Hierarchical Multi-head Attentive Network for Evidence-aware Fake News  Detection

Many multi-domain neural machine translation (NMT) models achieve knowledge
transfer by enforcing one encoder to learn shared embedding across domains.
However, this design lacks adaptation to individual domains. To overcome this
limitation, we propose a novel multi-domain NMT model using individual modules
for each domain, on which we apply word-level, adaptive and layer-wise domain
mixing. We first observe that words in a sentence are often related to multiple
domains. Hence, we assume each word has a domain proportion, which indicates
its domain preference. Then word representations are obtained by mixing their
embedding in individual domains based on their domain proportions. We show this
can be achieved by carefully designing multi-head dot-product attention modules
for different domains, and eventually taking weighted averages of their
parameters by word-level layer-wise domain proportions. Through this, we can
achieve effective domain knowledge sharing, and capture fine-grained
domain-specific knowledge as well. Our experiments show that our proposed model
outperforms existing ones in several NMT tasks.

本篇论文提出了使用独立词向量模块和基于词级别注意力机制的领域混合方法来实现多领域神经机器翻译的知识共享和精细化领域特定知识获取，实验结果表明该模型在多项 NMT 任务中表现优秀。