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Humans easily interpret expressions that describe unfamiliar situationscomposed from familiar parts ("greet the pink brontosaurus by the ferriswheel"). Modern neural networks, by contrast, struggle to interpret novelcompositions. In this paper, we introduce a new benchmark, gSCAN, forevaluating compositional generalization in situated language understanding.Going beyond a related benchmark that focused on syntactic aspects ofgeneralization, gSCAN defines a language grounded in the states of a gridworld, facilitating novel evaluations of acquiring linguistically motivatedrules. For example, agents must understand how adjectives such as 'small' areinterpreted relative to the current world state or how adverbs such as'cautiously' combine with new verbs. We test a strong multi-modal baselinemodel and a state-of-the-art compositional method finding that, in most cases,they fail dramatically when generalization requires systematic compositionalrules.

A Benchmark for Systematic Generalization in Grounded Language Understanding