Explaining Veracity Predictions with Evidence Summarization: A Multi-Task Model Approach

Anonymous

Abstract:

The rapid dissemination of misinformation through social media increased the importance of automated fact-checking.
Furthermore, studies on what deep neural models pay attention to when making predictions have increased in recent years. While significant progress has been made in this field, it has not yet reached a level of reasoning comparable to human reasoning. To address these gaps, we propose a multi-task explainable neural model for misinformation detection. Specifically, this work formulates an explanation generation process of the model's veracity prediction as a text summarization problem. Additionally, the performance of the proposed model is discussed on publicly available datasets and the findings are evaluated with related studies.

Paper Type: short

Research Area: Interpretability and Analysis of Models for NLP

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

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