We present a new AI task and baseline solution for Inter-Subjective Reasoning. We define inter-subjective information, to be a mixture of objective and subjective information possibly shared by different parties. Examples may include commodities and their objective properties as reported by IR (Information Retrieval) systems, that need to be cross-referenced with subjective user reviews from an online forum. For an AI system to successfully reason about both, it needs to be able to combine symbolic reasoning of objective facts with the shared consensus found on subjective user reviews. To this end we introduce the NeuroQL dataset and DSL (Domain-specific Language) as a baseline solution for this problem. NeuroQL is a neuro-symbolic language that extends logical unification with neural primitives for extraction and retrieval. It can function as a target for automatic translation of inter-subjective questions (posed in natural language) into the neuro-symbolic code that can answer them.

翻译：我们提出了一项新的人工智能任务及其基线解决方案——主体间推理。我们将主体间信息定义为可能由不同主体共享的客观与主观信息的混合体。例如，包括由信息检索系统报告的具有客观属性的商品，这些信息需要与在线论坛中的主观用户评价进行交叉参考。人工智能系统若要对两者进行成功推理，就必须能够将客观事实的符号推理与主观用户评价中发现的共享共识相结合。为此，我们引入了NeuroQL数据集和领域特定语言作为该问题的基线解决方案。NeuroQL是一种神经符号语言，它将逻辑统一扩展为用于提取和检索的神经原语。它可作为将主体间问题以自然语言表达自动翻译为能够回答这些问题的神经符号代码的目标。