Tag
CAMMAR introduces a representation learning framework that organizes Arabic metaphorical meaning into nested lexical, cultural, and metaphorical subspaces using a staged semantic curriculum, achieving strong metaphor detection (AUC up to 0.84) on a new span-annotated dataset.
This paper introduces MCLASH, a multilingual moral decision-making benchmark, and MET, a theory-grounded prompting method for culture-aware moral reasoning, along with MET-D, a self-distillation training method that improves multilingual moral reasoning across different model families and sizes.