56 papers with code • 0 benchmarks • 7 datasets
These leaderboards are used to track progress in Systematic Generalization
LibrariesUse these libraries to find Systematic Generalization models and implementations
Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities.
The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way.
In this paper, we introduce a new benchmark, gSCAN, for evaluating compositional generalization in situated language understanding.
In this work, we study how systematic the generalization of such models is, that is to which extent they are capable of handling novel combinations of known linguistic constructs.
Numerous models for grounded language understanding have been recently proposed, including (i) generic models that can be easily adapted to any given task and (ii) intuitively appealing modular models that require background knowledge to be instantiated.
Systematic Generalization refers to a learning algorithm's ability to extrapolate learned behavior to unseen situations that are distinct but semantically similar to its training data.