Commonsense Knowledge Graphs (CSKGs) are crucial for commonsense reasoning,
yet constructing them through human annotations can be costly. As a result,
various automatic methods have been proposed to construct CSKG with larger
semantic coverage. However, these unsupervised approaches introduce spurious
noise that can lower the quality of the resulting CSKG, which cannot be tackled
easily by existing denoising algorithms due to the unique characteristics of
nodes and structures in CSKGs. To address this issue, we propose Gold (Global
and Local-aware Denoising), a denoising framework for CSKGs that incorporates
entity semantic information, global rules, and local structural information
from the CSKG. Experiment results demonstrate that Gold outperforms all
baseline methods in noise detection tasks on synthetic noisy CSKG benchmarks.
Furthermore, we show that denoising a real-world CSKG is effective and even
benefits the downstream zero-shot commonsense question-answering task.

通过引入实体语义信息、全局规则和本地结构信息，我们提出了 Gold（全局和局部感知去噪）框架，用于在大规模语义覆盖范围下构建常识知识图谱。实验结果表明，Gold 在合成噪声常识知识图谱基准任务中优于所有基准方法，此外，我们展示了对真实世界常识知识图谱进行去噪的有效性，并进一步证明了在下游零样本常识问答任务中的益处。

金：全球和局部感知的常识知识图噪声检测框架

Gold: A Global and Local-aware Denoising Framework for Commonsense  Knowledge Graph Noise Detection

Zero-shot commonsense Question-Answering (QA) requires models to reason about
general situations beyond specific benchmarks. State-of-the-art approaches
fine-tune language models on QA pairs constructed from CommonSense Knowledge
Bases (CSKBs) to equip the models with more commonsense knowledge in a QA
context. However, current QA synthesis protocols may introduce noise from the
CSKBs and generate ungrammatical questions and false negative options, which
impede the model's ability to generalize. To address these issues, we propose
QADYNAMICS, a training dynamics-driven framework for QA diagnostics and
refinement. Our approach analyzes the training dynamics of each QA pair at both
the question level and option level, discarding machine-detectable artifacts by
removing uninformative QA pairs and mislabeled or false-negative options.
Extensive experiments demonstrate the effectiveness of our approach, which
outperforms all baselines while using only 33% of the synthetic data, even
including LLMs such as ChatGPT. Moreover, expert evaluations confirm that our
framework significantly improves the quality of QA synthesis. Our codes and
model checkpoints are available at
this https URL

利用 QADYNAMICS 框架，通过分析训练动态，从问题级别和选项级别剔除噪音，改进常识型问答合成的质量并在性能上超过基线模型。