Abstract
Deep transformer models excel at multi-label text classification but often violate domain logic that experts consider essential, an issue of particular concern in safety-critical applications. We propose a hybrid neuro-symbolic framework that integrates Answer Set Programming (ASP) with transformer-based learning on the Aviation Safety Reporting System (ASRS) corpus. Domain knowledge is formalized as weighted ASP rules and validated using the Clingo solver. These rules are incorporated in two complementary ways: (i) as rule-based data augmentation, generating logically consistent synthetic samples that improve label diversity and coverage; and (ii) as a fuzzy-logic regularizer, enforcing rule satisfaction in a differentiable form during fine-tuning. This design preserves the interpretability of symbolic reasoning while leveraging the scalability of deep neural architectures. We further tune per-class thresholds and report both standard classification metrics and logic-consistency rates. Compared to a strong Binary Cross-Entropy (BCE) baseline, our approach improves micro- and macro-F1 scores and achieves up to an 86% reduction in rule violations on the ASRS test set. To the best of our knowledge, this constitutes the first large-scale neuro-symbolic application to ASRS reports that unifies ASP-based reasoning, rule-driven augmentation, and differentiable transformer training for trustworthy, safety-critical NLP.
Abstract (translated)
深度变换器模型在多标签文本分类方面表现出色，但常常违反专家认为至关重要的领域逻辑，这对安全关键型应用尤为令人担忧。我们提出了一种将回答集编程（ASP）与基于变压器的学习相结合的混合神经符号框架，并应用于航空安全管理报告系统（ASRS）语料库。领域知识被形式化为加权ASP规则并通过Clingo求解器进行验证。这些规则以两种互补方式整合：(i) 作为基于规则的数据增强，生成逻辑一致的人工合成样本，增加标签多样性和覆盖率；以及 (ii) 作为一种模糊逻辑正则项，在微调过程中以可微分形式强制执行规则满足性。这种设计在利用深度神经架构的可扩展性的同时，保留了符号推理的解释能力。我们进一步调整每个类别的阈值，并报告标准分类指标和逻辑一致性率。与强大的二元交叉熵（BCE）基准相比，我们的方法提高了微平均F1分数和宏平均F1分数，在ASRS测试集上实现了高达86%的规则违反减少。据我们所知，这是第一个将ASP推理、基于规则的数据增强以及可微分变换器训练相结合的大规模神经符号应用于ASRS报告的安全关键型NLP应用。
URL
https://arxiv.org/abs/2510.05451
