  Success in natural language inference (NLI) should require a model to
understand both lexical and compositional semantics. However, through
adversarial evaluation, we find that several state-of-the-art models with
diverse architectures are over-relying on the former and fail to use the
latter. Further, this compositionality unawareness is not reflected via
standard evaluation on current datasets. We show that removing RNNs in existing
models or shuffling input words during training does not induce large
performance loss despite the explicit removal of compositional information.
Therefore, we propose a compositionality-sensitivity testing setup that
analyzes models on natural examples from existing datasets that cannot be
solved via lexical features alone (i.e., on which a bag-of-words model gives a
high probability to one wrong label), hence revealing the models' actual
compositionality awareness. We show that this setup not only highlights the
limited compositional ability of current NLI models, but also differentiates
model performance based on design, e.g., separating shallow bag-of-words models
from deeper, linguistically-grounded tree-based models. Our evaluation setup is
an important analysis tool: complementing currently existing adversarial and
linguistically driven diagnostic evaluations, and exposing opportunities for
future work on evaluating models' compositional understanding.
