Authors - Nazura Javed, Rida Javed Kutty, Muralidhara B L Abstract - The increasing availability of online information has made it easier to access diverse sources, but it has also introduced challenges in verifying the reliability and consistency of content. Conflicting statements across different sources often contribute to misinformation and make it difficult to establish factual accuracy. This study focuses on the problem of cross-document contradiction and inconsistency detection as a step toward improving fact verification in textual data. A two-stage pipeline is proposed in which semantically related sentence pairs are first retrieved from documents discussing the same event and then analyzed using Natural Language Inference (NLI) techniques to determine whether they express contradictory information. In contrast to conventional sentence-level contradiction detection, the proposed approach emphasizes document-level comparison to identify inconsistencies across independent sources. Two pre-trained transformer models, DistilBERT (DistilBERT-base-uncased) and RoBERTa (RoBERTa-base), are used for contradiction classification. The approach is evaluated on the SNLI dataset and the PHEME Rumor Dataset, which are widely used benchmarks for NLI and misinformation research. Experimental results show accuracies of 94.50% (F1 score 94.50%) on SNLI and 92.39% (F1-score 92.31%) on PHEME, indicating that the proposed framework is effective in identifying contradictions and supporting cross-document fact validation.
Open Zoom