Behavior-Based Ethical Understanding in Chinese Social News

Xuan Feng, Tianlong Gu, Xuguang Bao, Long Li

Abstract: Ethical understanding aims at morally analyzing and discriminating ethical scenarios described in natural language. By classifying behaviors that occur in ethical scenarios as ethical or unethical, ethical understanding empowers artificial intelligence systems to understand human values so as to discern right from wrong morally. However, most existing ethical understanding methods lack fine-grained analysis and cannot handle the problem that an ethical scenario may contain multiple behaviors with multiple polarities. In this paper, we introduce a novel natural language processing task, behavior-based ethical understanding (BEU), for mining the purpose relation(s) and ethical polarity of a specific behavior from the social news. It contains three subtasks: behavior term extraction (BTE) to extracts behavior terms, purpose relation inference (PRI) to identifies purposive relations among behaviors, and polarity discrimination (PD) to predicts the ethical polarities of behaviors, respectively. To perform this task, we constructed a Chinese BEU dataset, named FG-ETHICS. Besides, we propose a three-stage framework, BEU-BERT, based on the pre-trained language model BERT and deliberately designed downstream models for three subtasks. Experimental results show that the proposed framework achieves the best performance from the BTE and PD tasks, and achieves a promising performance of 75% on the PRI task.