CAMMAR

CAMMAR: Culture-Aware Matryoshka embeddings for Arabic metaphor detection

Researchers introduce CAMMAR, a representation learning framework that organizes Arabic language meaning into nested lexical, cultural, and metaphorical embedding subspaces using a staged semantic curriculum inspired by Al-Jurjani's classical theory of nazum. The framework addresses 'semantic smearing' in current Arabic LMs and yields a training-free geometric metaphoricity measure. On a new span-annotated Arabic metaphor dataset, the geometric readout achieves AUC up to 0.84 and correctly ranks figurative over literal counterparts in 82.6% of pairs under paired supervision, though performance collapses to chance under unsupervised domain contrast alone.