We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on ten distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.
@article{arxiv.1805.01042,
title = {Hypothesis Only Baselines in Natural Language Inference},
author = {Adam Poliak and Jason Naradowsky and Aparajita Haldar and Rachel Rudinger and Benjamin Van Durme},
journal= {arXiv preprint arXiv:1805.01042},
year = {2018}
}