用符号逻辑增强弱监督，让模型更懂标签背后的语义关系。
Neuro-symbolic Weak Supervision: Theory and Semantics
- 结合逻辑编程构建标签转移的约束框架，明确分类器语义。
- 可从观察数据中推断标签转换规则和实例分类归属。
- 适合研究弱监督语义错误诊断的学者，尤其关注标签模糊问题。
弱监督使模型能从有限或噪声标签中学习，但在多实例部分标签学习（MI-PLL）中面临监督信号模糊与实例-标签映射不确定的挑战。本文提出一种神经符号框架的语义形式化，引入归纳逻辑编程（ILP）对MI-PLL进行结构化建模。该框架通过关系约束定义标签转移的假设空间，形式化单实例分类器的语义，并为弱监督推理提供关系骨架。研究了两类归纳任务：从观测数据与分类器谓词中推断标签转移谓词，以及从观测数据与转移谓词中推断实例级分类分配。该形式化语义支持约束设定、一致性检查，并揭示袋级准确率无法捕捉的语义失效模式。
原文摘要 · Abstract (English)
Weak supervision enables machine learning models to learn from limited or noisy labels, but it introduces challenges in reliability and semantic clarity, particularly in multi-instance partial label learning (MI-PLL), where models must resolve both ambiguous supervision signals and uncertain instance-label mappings. This paper proposes a semantics for a neuro-symbolic framework that integrates inductive logic programming (ILP) to structure MI-PLL through relational constraints. In this formulation, ILP defines a hypothesis space over label transitions, formalizes the semantics of per-instance classifiers and provides a relational scaffold for reasoning about weak supervision. Two inductive tasks are studied in this framework: inferring the transition predicate from the observed and classifier predicates, and inferring instance-level classifier assignments from the observed and transition predicates. This formal semantics facilitates constraint specification, consistency checking and the diagnosis of semantic failure modes that bag-level accuracy alone may conceal.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。