MOKA: Moral Knowledge Augmentation for Moral Event Extraction
Xinliang Frederick ZhangWinston WuNick BeauchampLu Wang
Xinliang Frederick ZhangWinston WuNick BeauchampLu Wang
Nov 2023
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摘要原文
News media employ moral language to create memorable stories, and readers often engage with the content that align with their values. Moral theories have been applied to news analysis studying moral values in isolation, while the intricate dynamics among participating entities in shaping moral events have been overlooked. This is mainly due to the use of obscure language to conceal evident ideology and values, coupled with the insufficient moral reasoning capability in most existing NLP systems, where LLMs are no exception. To study this phenomenon, we first annotate a new dataset, MORAL EVENTS, consisting of 5,494 structured annotations on 474 news articles by diverse US media across the political spectrum. We further propose MOKA, a moral event extraction framework with MOral Knowledge Augmentation, that leverages knowledge derived from moral words and moral scenarios. Experimental results show that MOKA outperforms competitive baselines across three moral event understanding tasks. Further analyses illuminate the selective reporting of moral events by media outlets of different ideological leanings, suggesting the significance of event-level morality analysis in news. Our datasets and codebase are available at https://github.com/launchnlp/MOKA.
