1 research outputs found
Probing Natural Language Inference Models through Semantic Fragments
Do state-of-the-art models for language understanding already have, or can
they easily learn, abilities such as boolean coordination, quantification,
conditionals, comparatives, and monotonicity reasoning (i.e., reasoning about
word substitutions in sentential contexts)? While such phenomena are involved
in natural language inference (NLI) and go beyond basic linguistic
understanding, it is unclear the extent to which they are captured in existing
NLI benchmarks and effectively learned by models. To investigate this, we
propose the use of semantic fragments---systematically generated datasets that
each target a different semantic phenomenon---for probing, and efficiently
improving, such capabilities of linguistic models. This approach to creating
challenge datasets allows direct control over the semantic diversity and
complexity of the targeted linguistic phenomena, and results in a more precise
characterization of a model's linguistic behavior. Our experiments, using a
library of 8 such semantic fragments, reveal two remarkable findings: (a)
State-of-the-art models, including BERT, that are pre-trained on existing NLI
benchmark datasets perform poorly on these new fragments, even though the
phenomena probed here are central to the NLI task. (b) On the other hand, with
only a few minutes of additional fine-tuning---with a carefully selected
learning rate and a novel variation of "inoculation"---a BERT-based model can
master all of these logic and monotonicity fragments while retaining its
performance on established NLI benchmarks.Comment: AAAI camera-ready versio