Parallel neurosymbolic architectures have been applied effectively in NLP by distilling knowledge from a logic theory into a deep model.However, prior art faces several limitations including supporting restricted forms of logic theories and relying on the assumption of independence between the logic and the deep network. We present Concordia, a framework overcoming the limitations of prior art. Concordia is agnostic both to the deep network and the logic theory offering support for a wide range of probabilistic theories. Our framework can support supervised training of both components and unsupervised training of the neural component. Concordia has been successfully applied to tasks beyond NLP and data classification, improving the accuracy of state-of-the-art on collective activity detection, entity linking and recommendation tasks.

翻译：并行神经符号架构通过将逻辑理论中的知识蒸馏到深度模型中，已有效应用于自然语言处理领域。然而，现有技术面临若干局限，包括仅支持受限的逻辑理论形式以及依赖逻辑系统与深度网络之间的独立性假设。我们提出Concordia框架，该框架克服了现有技术的局限性。Concordia对深度网络和逻辑理论均保持无偏性，支持广泛的概率理论。该框架可同时支持两个组件的监督训练以及神经组件的无监督训练。Concordia已成功应用于自然语言处理和数据分类之外的领域，在群体活动检测、实体链接和推荐任务中提升了现有最佳模型的准确率。