NeSyA：神经符号自动机
NeSyA: Neurosymbolic Automata
AI总结：
针对现有神经符号AI缺乏时序问题适配系统的问题，提出结合符号自动机与神经感知的端到端可微混合模型NeSyA，在合成基准和真实事件识别任务中分别展现出性能与泛化性优势。
摘要：
神经符号（NeSy）AI已成为融合神经推理与符号推理的一个颇具前景的方向。遗憾的是，目前针对序列/时序问题定制的神经符号系统的研发工作还很少。我们认为符号自动机（它结合了自动机的时序推理能力与命题逻辑的静态推理能力）是表达时序领域知识的合适形式化体系。针对序列分类与标注任务，我们证明符号自动机可与基于神经网络的感知模块集成，在概率语义下构建端到端可微模型。我们提出的混合模型名为NeSyA（神经符号自动机），在合成基准测试中，其扩展性或准确率均优于以往的神经符号系统；在真实事件识别任务中，与纯神经网络系统相比，它在泛化性方面更具优势。
英文摘要：
Neurosymbolic (NeSy) AI has emerged as a promising direction to integrate neural and symbolic reasoning. Unfortunately, little effort has been given to developing NeSy systems tailored to sequential/temporal problems. We identify symbolic automata (which combine the power of automata for temporal reasoning with that of propositional logic for static reasoning) as a suitable formalism for expressing knowledge in temporal domains. Focusing on the task of sequence classification and tagging we show that symbolic automata can be integrated with neural-based perception, under probabilistic semantics towards an end-to-end differentiable model. Our proposed hybrid model, termed NeSyA (Neuro Symbolic Automata) is shown to either scale or perform more accurately than previous NeSy systems in a synthetic benchmark and to provide benefits in terms of generalization compared to purely neural systems in a real-world event recognition task.
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