Current open-domain neural semantics parsers show impressive performance. However,
closer inspection of the symbolic meaning representations they produce reveals significant …

We present the Granular AMR Parsing Evaluation Suite (GrAPES), a challenge set for
Abstract Meaning Representation (AMR) parsing with accompanying evaluation metrics …

Compositionality is a core property of natural language, and compositional behavior (CB) is
a crucial goal for modern NLP systems. The research literature, however, includes …

Due to the finite nature of any evidence used in learning, systematic generalization is
crucially reliant on the presence of inductive bias ( Mitchell, 1980). We examine inductive …

Models need appropriate inductive biases to effectively learn from small amounts of data
and generalize systematically outside of the training distribution. While Transformers are …

Discourse Entity (DE) recognition is the task of identifying novel and known entities
introduced within a text. While previous work has found that large language models have …

Compositional generalization refers to the ability to generalize to novel combinations of
previously observed words and syntactic structures. Since it is regarded as a desired …

Compositional generalization that requires production and comprehension of novel
_structures_ through observed constituent parts has been shown to be challenging for even …

The ability to predict an NLP model's accuracy on unseen, potentially out-of-distribution data
is a prerequisite for trustworthiness. We present a novel model that establishes upper and …

Traditional linguistic theories have long posited that human language competence is
founded on innate structural properties and symbolic representations. However, Transformer …