CAMMAR: Culture-Aware Matryoshka for Metaphorical Arabic Representations

• URL: http://arxiv.org/abs/2607.15847v1
• Date: Fri, 17 Jul 2026 11:05:51 GMT
• Title: CAMMAR: Culture-Aware Matryoshka for Metaphorical Arabic Representations
• Abstract summary: CAMAUCMAR organizes meaning into nested lexical, cultural, and metaphorical embedding subspaces.<n>The geometric readout detects metaphor well above chance when the inter-layer geometry is shaped by paired supervision.<n>A controlled ablation shows that grounding the lexical layer in morphological roots gives a small but consistent gain.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Metaphor in Arabic is a culturally grounded mechanism for constructing meaning, encoding cultural knowledge that shapes interpretation. Yet current Arabic language models typically collapse lexical, cultural, and metaphorical information into a single representational space, a phenomenon we term "semantic smearing". We introduce CAMMAR (Culture-Aware Matryoshka for Metaphorical Arabic Representations), a representation learning framework that organizes meaning into nested lexical, cultural, and metaphorical embedding subspaces through a staged semantic curriculum. The design implements compositional principles of Al-Jurjani's theory of nazum, modeling figurative meaning as compositionally grounded in prior semantic relations, and yields a training-free geometric measure of metaphoricity based on the distance between lexical and metaphorical representations. Evaluated on a new span-annotated Arabic metaphor set as word-matched figurative/literal pairs, the geometric readout detects metaphor well above chance when the inter-layer geometry is shaped by paired supervision (AUC up to 0.84; figurative outscores its literal counterpart for the same word in 82.6\% of pairs), but sits at chance under an unsupervised domain contrast alone, a clean separation between a legible-under-supervision regime and a non-emergent one. A controlled ablation shows that grounding the lexical layer in morphological roots gives a small but consistent gain, an effect absent from direct probing that reflects the layer's quality as a measurement anchor. We will release the datasets, cultural concept inventory, and code upon acceptance.

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