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The ability to reason with multiple hierarchical structures is an attractiveand desirable property of sequential inductive biases for natural languageprocessing. Do the state-of-the-art Transformers and LSTM architecturesimplicitly encode for these biases? To answer this, we propose ORCHARD, adiagnostic dataset for systematically evaluating hierarchical reasoning instate-of-the-art neural sequence models. While there have been prior evaluationframeworks such as ListOps or Logical Inference, our work presents a novel andmore natural setting where our models learn to reason with multiple explicithierarchical structures instead of only one, i.e., requiring the ability to doboth long-term sequence memorizing, relational reasoning while reasoning withhierarchical structure. Consequently, backed by a set of rigorous experiments,we show that (1) Transformer and LSTM models surprisingly fail in systematicgeneralization, and (2) with increased references between hierarchies,Transformer performs no better than random.

ORCHARD: A Benchmark For Measuring Systematic Generalization of Multi-Hierarchical Reasoning