用知识图谱提升印尼语新冠事实核查的准确率
Enhancing Natural Language Inference Performance with Knowledge Graph for COVID-19 Automated Fact-Checking in Indonesian Language
- 引入印尼语新冠知识图谱，增强自然语言推理能力
- 在自建数据集上达到0.8616的最高准确率
- 适合关注多语言事实核查与知识增强模型的研究者
自动化事实核查是应对互联网上新冠虚假信息传播的关键策略。现有系统通常依赖深度学习方法，通过自然语言推理（NLI）基于支持证据验证信息真实性。然而，深度学习常因训练中知识不足导致性能停滞。本研究提出将知识图谱（KG）作为外部知识，以提升印尼语环境下新冠事实核查的NLI表现。模型包含三个模块：事实模块处理知识图谱信息，NLI模块分析前提与假设间的语义关系，两模块的表示向量拼接后输入分类器生成最终结果。模型在自建的印尼语新冠事实核查数据集及印尼语新冠知识图谱上训练，结果表明引入知识图谱可显著提升性能，最高准确率达0.8616，证明知识图谱对增强自动事实核查中的NLI能力具有重要价值。
原文摘要 · Abstract (English)
Automated fact-checking is a key strategy to overcome the spread of COVID-19 misinformation on the internet. These systems typically leverage deep learning approaches through Natural Language Inference (NLI) to verify the truthfulness of information based on supporting evidence. However, one challenge that arises in deep learning is performance stagnation due to a lack of knowledge during training. This study proposes using a Knowledge Graph (KG) as external knowledge to enhance NLI performance for automated COVID-19 fact-checking in the Indonesian language. The proposed model architecture comprises three modules: a fact module, an NLI module, and a classifier module. The fact module processes information from the KG, while the NLI module handles semantic relationships between the given premise and hypothesis. The representation vectors from both modules are concatenated and fed into the classifier module to produce the final result. The model was trained using the generated Indonesian COVID-19 fact-checking dataset and the COVID-19 KG Bahasa Indonesia. Our study demonstrates that incorporating KGs can significantly improve NLI performance in fact-checking, achieving the best accuracy of 0.8616. This suggests that KGs are a valuable component for enhancing NLI performance in automated fact-checking.
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