  Given comparative text, comparative relation extraction aims to extract two
targets (\eg two cameras) in comparison and the aspect they are compared for
(\eg image quality). The extracted comparative relations form the basis of
further opinion analysis.Existing solutions formulate this task as a sequence
labeling task, to extract targets and aspects. However, they cannot directly
extract comparative relation(s) from text. In this paper, we show that
comparative relations can be directly extracted with high accuracy, by
generative model. Based on GPT-2, we propose a Generation-based Comparative
Relation Extractor (GCRE-GPT). Experiment results show that \modelname achieves
state-of-the-art accuracy on two datasets.
