  Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) is
the task of predicting the entailment relation between a pair of sentences
(premise and hypothesis). This task has been described as a valuable testing
ground for the development of semantic representations, and is a key component
in natural language understanding evaluation benchmarks. Models that understand
entailment should encode both, the premise and the hypothesis. However,
experiments by Poliak et al. revealed a strong preference of these models
towards patterns observed only in the hypothesis, based on a 10 dataset
comparison. Their results indicated the existence of statistical irregularities
present in the hypothesis that bias the model into performing competitively
with the state of the art. While recast datasets provide large scale generation
of NLI instances due to minimal human intervention, the papers that generate
them do not provide fine-grained analysis of the potential statistical patterns
that can bias NLI models. In this work, we analyze hypothesis-only models
trained on one of the recast datasets provided in Poliak et al. for word-level
patterns. Our results indicate the existence of potential lexical biases that
could contribute to inflating the model performance.
