Robust and resource-efficient table-based fact verification through multi-aspect adversarial contrastive learning

Ruiheng Liu, Yu Zhang, Bailong Yang, Qi Shi, Luogeng Tian

Abstract: Highlights•We propose Macol, which verifies statement accuracy by integrating relevant tables.•By fusing multi-aspect reasoning clues, Macol guides us to obtain key insights.•Using auto-generated adversarial instances, Macol enables fine-grained reasoning.•Macol excels in benchmarks, performs well with limited resources, and is robust.