Modeling Factual Claims with Semantic Frames
In this paper, we introduce an extension of the Berkeley FrameNet for the structured and semantic modeling of factual claims. Modeling is a robust tool that can be leveraged in many different tasks such as matching claims to existing fact-checks and translating claims to structured queries. Our work introduces 11 new manually crafted frames along with 9 existing FrameNet frames, all of which have been selected with fact-checking in mind. Along with these frames, we are also providing 2,540 fully annotated sentences, which can be used to understand how these frames are intended to work and to train machine learning models. Finally, we are also releasing our annotation tool to facilitate other researchers to make their own local extensions to FrameNet.
Code (0)
등록된 구현이 없습니다.
Tasks
Fact CheckingSimilar Papers 제목 키워드 기반
Automatic Fact-Checking with Frame-Semantics
We propose a novel paradigm for automatic fact-checking that leverages frame semantics to enhance the structured understanding of claims, addressing the challenges posed by misinformation in today's information ecosystem…
Fact CheckingMisinformationRetrievalImproving Factual Error Correction by Learning to Inject Factual Errors
Factual error correction (FEC) aims to revise factual errors in false claims with minimal editing, making them faithful to the provided evidence. This task is crucial for alleviating the hallucination problem encountered…
HallucinationInterpretable Automatic Fine-grained Inconsistency Detection in Text Summarization
Existing factual consistency evaluation approaches for text summarization provide binary predictions and limited insights into the weakness of summarization systems. Therefore, we propose the task of fine-grained inconsi…
Semantic Role LabelingText SummarizationFactLens: Benchmarking Fine-Grained Fact Verification
Large Language Models (LLMs) have shown impressive capability in language generation and understanding, but their tendency to hallucinate and produce factually incorrect information remains a key limitation. To verify LL…
BenchmarkingFact VerificationText GenerationDecMetrics: Structured Claim Decomposition Scoring for Factually Consistent LLM Outputs
Claim decomposition plays a crucial role in the fact-checking process by breaking down complex claims into simpler atomic components and identifying their unfactual elements. Despite its importance, current research prim…