MOKA: Moral Knowledge Augmentation for Moral Event Extraction
This work addresses the problem of extracting moral events from news for NLP researchers, offering a dataset and method to analyze selective reporting, though it is incremental in applying knowledge augmentation to a specific domain.
The authors tackled the challenge of detecting moral values in news articles by creating a new dataset, MORAL EVENTS, with 5,494 annotations, and proposed MOKA, a framework that outperforms baselines in moral event understanding tasks.
News media often strive to minimize explicit moral language in news articles, yet most articles are dense with moral values as expressed through the reported events themselves. However, values that are reflected in the intricate dynamics among participating entities and moral events are far more challenging for most NLP systems to detect, including LLMs. To study this phenomenon, we annotate a new dataset, MORAL EVENTS, consisting of 5,494 structured event 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, which leverages knowledge derived from moral words and moral scenarios to produce structural representations of morality-bearing events. Experiments show that MOKA outperforms competitive baselines across three moral event understanding tasks. Further analysis shows even ostensibly nonpartisan media engage in the selective reporting of moral events. Our data and codebase are available at https://github.com/launchnlp/MOKA.