Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims

Abstract

False claims that have been previously fact-checked can still spread on social media. To mitigate their continual spread, detecting previously fact-checked claims is indispensable. Given a claim, existing works focus on providing evidence for detection by reranking candidate fact-checking articles (FC-articles) retrieved by BM25. However, these performances may be limited because they ignore the following characteristics of FC-articles: (1) claims are often quoted to describe the checked events, providing lexical information besides semantics; (2) sentence templates to introduce or debunk claims are common across articles, providing pattern information. Models that ignore the two aspects only leverage semantic relevance and may be misled by sentences that describe similar but irrelevant events. In this paper, we propose a novel reranker, MTM (Memory-enhanced Transformers for Matching) to rank FC-articles using key sentences selected with event (lexical and semantic) and pattern information. For event information, we propose a ROUGE-guided Transformer which is finetuned with regression of ROUGE. For pattern information, we generate pattern vectors for matching with sentences. By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns. Experiments on two real-world datasets show that MTM outperforms existing methods. Human evaluation proves that MTM can capture key sentences for explanations. The code and the dataset are at https://github.com/ICTMCG/MTM.

arXiv commentACL-IJCNLP 2021 Main Conference Long Paper

References in corpus (11)

• Adam: A Method for Stochastic Optimization
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• BERTScore: Evaluating Text Generation with BERT
• Unsupervised Data Augmentation for Consistency Training
• Passage Re-ranking with BERT
• FANG: Leveraging Social Context for Fake News Detection Using Graph Representation
• Multi-Stage Document Ranking with BERT
• Simple Applications of BERT for Ad Hoc Document Retrieval
• Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
• Complementing Lexical Retrieval with Semantic Residual Embedding
• A Kernel of Truth: Determining Rumor Veracity on Twitter by Diffusion Pattern Alone

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• Combating Online Misinformation Videos: Characterization, Detection, and Future Directions
• MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection
• Multilingual Previously Fact-Checked Claim Retrieval
• A Survey on Automatic Credibility Assessment Using Textual Credibility Signals in the Era of Large Language Models
• Autonomation, Not Automation: Activities and Needs of European Fact-checkers as a Basis for Designing Human-Centered AI Systems