arXiv:2607.01762v1 Announce Type: new Abstract: 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 nonnegati
This paper introduces a novel approach to address a known limitation in representation learning for directed relations, suggesting an incremental advancement within AI research.
Improving how AI models understand directed relationships can lead to more accurate and nuanced applications across various data types, from language to knowledge graphs.
The proposed 'role-aware neural convex divergence head' offers a new method for modeling asymmetric relationships, enhancing the representational capabilities of AI systems.
- · AI researchers
- · NLP developers
- · Data scientists
- · Developers of knowledge graphs
- · Systems relying solely on symmetric representation methods
More accurate representation of directed relationships in complex AI models.
Improved performance in applications like lexical entailment, ontology modeling, and citation analysis.
The development of more sophisticated AI agents capable of understanding intricate relational data structures.
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