From Polarity to Intensity: Mining Morality from Semantic Space (2022)

Abstract: Most works on computational morality focus on moral polarity recognition, i.e., distinguishing right from wrong. However, a discrete polarity label is not informative enough to reflect morality as it does not contain any degree or intensity information. Existing approaches to compute moral intensity are limited to word-level measurement and heavily rely on human labelling. In this paper, we propose MoralScore, a weakly-supervised framework that can automatically measure moral intensity from text. It only needs moral polarity labels, which are more robust and easier to acquire. Besides, the framework can capture latent moral information not only from words but also from sentence-level semantics which can provide a more comprehensive measurement. To evaluate the performance of our method, we introduce a set of evaluation metrics and conduct extensive experiments. Results show that our method achieves good performance on both automatic and human evaluations.

## introduction moral intensity is a degree of feeling that a person has about a behaviour @xcite . as shown in figure 1 , although speeding on streets and killing a child are both immoral, the latter is more severe in most people's perception. understanding the above difference is an ability that humans have gradually developed in everyday life. it affects individuals' ethical judgments and reflects the ideology of our society @xcite . as ai gets ever more involved in people's lives, it has become increasingly important for machine to acquire this ability and behave ethically. researchers have studied the problem from early rule-based methods to today's deep learning-based paradigms @xcite . it remains a fundamental but unsolved problem in computational morality @xcite . ivy puts the rubbish in the dustbin. ## related work computational morality has received increased attention recently, especially in the nlp community @xcite . there are several relevant datasets concerning different aspects of this topic @xcite @xcite . the detection of moral polarity is a primary line of work, which is often modelled as a supervised classification task @xcite @xcite . unlike the above, we focus on measuring moral intensity. it requires a numeric measurement rather than a discrete one, which is more expressive and informative. @xcite introduces moralstrength to study word-level strength related to moral traits by crowdsourcing. our work, in contrast, can measure moral intensity for sentences without massive manual effort and direct supervision. another line of work uses nlp tools to analyze morality in text, largely based on the moral foundations dictionary @xcite . for example, it has been used in analyzing moral rhetoric in social media @xcite , moral sentiment in argumentation @xcite , and moral framing in political tweets @xcite . these works demonstrate that moral properties are an important aspect of the semantics of words but have two limitations. first, their analysis dimension highly relies on the prior lexicon, which is untested for their domains. in our work, we do not require a pre-defined domain dictionary. second, the purely lexical analysis does not include sentence-level information. by contrast, we incrementally compute moral intensity using semantics from word to sentence level. ## discussion as shown in our framework largely depends on latent moral information from language representation. it may inherit some potential biases (e.g., gender) that exist in the representation. moreover, moral judgments can differ across time, space, and culture @xcite , which is beyond the scope of this paper but is a valuable direction in future work. ## conclusion the computational study of moral intensity remains a challenging yet less explored topic in the field of nlp. we present moralscore, which can measure moral intensity for text. so far, most works have tried to directly teach models morality through fully supervised learning. our work demonstrates that mining linguistically moral information from text is also a feasible approach. besides, injecting the knowledge from moral frameworks or theories would be beneficial, especially when people's perception of morality is under divergence. we hope our findings can inspire future work on this topic.