DTDA: Dual-channel Triple-to-quintuple Data Augmentation for Comparative Opinion Quintuple Extraction

Qingting Xu, Kaisong Song, Yangyang Kang, Chaoqun Liu, Yu Hong, Guodong Zhou

Abstract: Highlights•We propose an end-to-end approach to solve error propagation problem.•We utilize ChatGPT to automatically construct triplet dataset.•We propose a novel dual-channel triplet-to-quintuple data augmentation for COQE task.•Experimental results on three datasets demonstrate the effectiveness of the method.•Our data augmentation technique can be effectively applied to current generation-based methods as well.