ProoFVer: Natural Logic Theorem Proving for Fact Verification

By Amrith Krishna, Sebastian Riedel, Andreas Vlachos. 2021

87 citations recorded by the source corpus.

Abstract Fact verification systems typically rely on neural network classifiers for veracity prediction, which lack explainability. This paper proposes ProoFVer, which uses a seq2seq model to generate natural logic-based inferences as proofs. These proofs consist of lexical mutations between spans in the claim and the evidence retrieved, each marked with a natural logic operator. Claim veracity is determined solely based on the sequence of these operators. Hence, these proofs are faithful explanations, and this makes ProoFVer faithful by construction. Currently, ProoFVer has the highest label accuracy and the second best score in the FEVER leaderboard. Furthermore, it improves by 13.21% points over the next best model on a dataset with counterfactual instances, demonstrating its robustness. As explanations, the proofs show better overlap with human rationales than attention-based highlights and the proofs help humans predict model decisions correctly more often than using the evidence directly.1

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

FacTool: Factuality Detection in Generative AI - A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios (2023, background): “1, the creation of the FEVER dataset (Thorne et al., 2018a) spawned models (Zhong et al., 2020; Krishna et al., 2022) that determine whether a given fine-grained claim made based on Wikipedia1 articles is correct.”