A Multi-Level Attention Model for Evidence-Based Fact Checking
Canasai Kruengkrai, Junichi Yamagishi, Xin Wang · 2021
Evidence-based fact checking aims to verify the truthfulness of a claim against evidence extracted from textual sources.Learning a representation that effectively captures relations between a claim and evidence can be challenging.Recent state-of-the-art approaches have developed increasingly sophisticated models based on graph structures.We present a simple model that can be trained on sequence structures.Our model enables inter-sentence attentions at different levels and can benefit from joint training.Results on a large-scale dataset for Fact Extraction and VERification (FEVER) show that our model outperforms the graphbased approaches and yields 1.09% and 1.42% improvements in label accuracy and FEVER score, respectively, over the best published model. 1