The proliferation of social media platforms has fueled the rapid
dissemination of fake news, posing threats to our real-life society. Existing
methods use multimodal data or contextual information to enhance the detection
of fake news by analyzing news content and/or its social context. However,
these methods often overlook essential textual news content (articles) and
heavily rely on sequential modeling and global attention to extract semantic
information. These existing methods fail to handle the complex, subtle twists
in news articles, such as syntax-semantics mismatches and prior biases, leading
to lower performance and potential failure when modalities or social context
are missing. To bridge these significant gaps, we propose a novel multi-hop
syntax aware fake news detection (MSynFD) method, which incorporates
complementary syntax information to deal with subtle twists in fake news.
Specifically, we introduce a syntactical dependency graph and design a
multi-hop subgraph aggregation mechanism to capture multi-hop syntax. It
extends the effect of word perception, leading to effective noise filtering and
adjacent relation enhancement. Subsequently, a sequential relative
position-aware Transformer is designed to capture the sequential information,
together with an elaborate keyword debiasing module to mitigate the prior bias.
Extensive experimental results on two public benchmark datasets verify the
effectiveness and superior performance of our proposed MSynFD over
state-of-the-art detection models.

我们提出了一种新颖的多跳语法感知假新闻检测（MSynFD）方法，该方法利用互补的语法信息来处理假新闻中的微妙转折。通过引入语法依赖图和设计多跳子图聚合机制来捕捉多跳语法，扩展了词感知的效果，从而实现有效的噪声过滤和相邻关系增强。随后，设计了一个顺序相对位置感知的 Transformer 来捕捉顺序信息，并结合一个精心设计的关键词去偏模块来减少先验偏差。在两个公共基准数据集上的大量实验结果验证了我们提出的 MSynFD 方法的有效性和卓越性能，超过了最先进的检测模型。

MSynFD: 多跳句法感知假新闻检测

MSynFD: Multi-hop Syntax aware Fake News Detection

Data augmentation is an effective performance enhancement in neural machine
translation (NMT) by generating additional bilingual data. In this paper, we
propose a novel data augmentation enhancement strategy for neural machine
translation. Different from existing data augmentation methods which simply
choose words with the same probability across different sentences for
modification, we set sentence-specific probability for word selection by
considering their roles in sentence. We use dependency parse tree of input
sentence as an effective clue to determine selecting probability for every
words in each sentence. Our proposed method is evaluated on WMT14
English-to-German dataset and IWSLT14 German-to-English dataset. The result of
extensive experiments show our proposed syntax-aware data augmentation method
may effectively boost existing sentence-independent methods for significant
translation performance improvement.

本文提出了一种新的句法感知数据增强策略，使用输入句子的依存句法树来确定每个句子中单词的选择概率，以有效增强神经机器翻译的翻译性能。实验表明，所提出的数据增强方法可显著提高基于句子独立的方法的翻译性能。