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Found 7 papers, 3 papers with code
Towards a Framework for Evaluating Explanations in Automated Fact Verification
As deep neural models in NLP become more complex, and as a consequence opaque, the necessity to interpret them becomes greater.
Little Giants: Exploring the Potential of Small LLMs as Evaluation Metrics in Summarization in the Eval4NLP 2023 Shared Task
This paper describes and analyzes our participation in the 2023 Eval4NLP shared task, which focuses on assessing the effectiveness of prompt-based techniques to empower Large Language Models to handle the task of quality estimation, particularly in the context of evaluating machine translations and summaries.
Policy Compliance Detection via Expression Tree Inference
In this work, we learn how to infer expression trees automatically from policy texts.
Graph Reasoning with Context-Aware Linearization for Interpretable Fact Extraction and Verification
This paper presents an end-to-end system for fact extraction and verification using textual and tabular evidence, the performance of which we demonstrate on the FEVEROUS dataset.
Explainable Automated Fact-Checking: A Survey
A number of exciting advances have been made in automated fact-checking thanks to increasingly larger datasets and more powerful systems, leading to improvements in the complexity of claims which can be accurately fact-checked.
Explainable Automated Fact-Checking for Public Health Claims
We present the first study of explainable fact-checking for claims which require specific expertise.
Gradual Argumentation Evaluation for Stance Aggregation in Automated Fake News Detection
One very important stage in employing stance detection for fake news detection is the aggregation of multiple stance labels from different text sources in order to compute a prediction for the veracity of a claim.
