Recent advances in NLP show that language models retain a discernible level of knowledge in deontological ethics and moral norms. However, existing works often treat morality as binary, ranging from right to wrong. This simplistic view does not capture the nuances of moral judgment. Pluralist moral philosophers argue that human morality can be deconstructed into a finite number of elements, respecting individual differences in moral judgment. In line with this view, we build a pluralist moral sentence embedding space via a state-of-the-art contrastive learning approach. We systematically investigate the embedding space by studying the emergence of relationships among moral elements, both quantitatively and qualitatively. Our results show that a pluralist approach to morality can be captured in an embedding space. However, moral pluralism is challenging to deduce via self-supervision alone and requires a supervised approach with human labels.

通过构建由对比学习方法成立的主观道德句子嵌入空间，我们系统地研究了道德元素之间的关系的涌现，定量和定性地证明了批判性的道德观可以在嵌入空间中捕捉到；然而，道德多元主义很难仅通过自我监督方法推导出，并且需要采用人工标签的监督方法。

道德不是二元的：使用对比学习来构建多元化的道德句子嵌入空间