DEFAULT at CheckThat! 2024: Retrieval Augmented Classification using Differentiable Top-K Operator for Rumor Verification based on Evidence from Authorities

Sayanta Adhikari, Himanshu, Rupa Kumari, Shrey Satapara, Maunendra Sankar Desarkar

Abstract:

The paper describes Team DEFAULT’s submission at CheckThat! 2024 Task-5 on Rumor Verification based on
Evidence from Authorities: In this paper, we present an approach for rumor verification on Twitter, focusing on
integrating evidence from authoritative accounts to determine the veracity of rumors. We propose an architecture
and a training regime as the preferred method to ensure seamless gradient flow. We formulate rumor verification
using evidence from authorities as a Retrieval-Augmented Classification (RAC) task. By re-parameterizing the
Top-K operator and applying Entropy-based Smoothing, our method addresses the discontinuity issues faced after
retrieval, enhancing the accuracy of rumor verification. Using this classification-aware retrieval, the retriever
achieves Recall@5 0.778, outperforming the baseline, placing team DEFAULT third on the test data leaderboard
for retrieval. For classification, our approach performs on par with the baseline.