Role-Aware Neural Convex Divergence Heads for Asymmetric Representation Learning
Many representation learning problems involve directed relations, such as lexical entailment, sentence entailment, ontology hierarchy, and citation links. Standard Euclidean, cosine, and Mahalanobis heads are symmetric, while generic neural scorers can model directionality but provide limited geometric structure. This paper proposes a role-aware neural convex divergence head for asymmetric representation learning. The head applies source- and target-role projections before evaluating an input-convex neural Bregman divergence, yielding a nonnegative structured score in the role-projected space. We characterize its projected-space identity, source-role convexity, directional-gap decompositi...
| Authors | He Huang, Lu Shen, Yunfeng Huang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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