  Natural Language Inference (NLI) datasets often contain hypothesis-only
biases---artifacts that allow models to achieve non-trivial performance without
learning whether a premise entails a hypothesis. We propose two probabilistic
methods to build models that are more robust to such biases and better transfer
across datasets. In contrast to standard approaches to NLI, our methods predict
the probability of a premise given a hypothesis and NLI label, discouraging
models from ignoring the premise. We evaluate our methods on synthetic and
existing NLI datasets by training on datasets containing biases and testing on
datasets containing no (or different) hypothesis-only biases. Our results
indicate that these methods can make NLI models more robust to dataset-specific
artifacts, transferring better than a baseline architecture in 9 out of 12 NLI
datasets. Additionally, we provide an extensive analysis of the interplay of
our methods with known biases in NLI datasets, as well as the effects of
encouraging models to ignore biases and fine-tuning on target datasets.
