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

NeuroQL: A Neuro-Symbolic Language and Dataset for Inter-Subjective Reasoning