  Natural language explanations (NLEs) are a special form of data annotation in
which annotators identify rationales (most significant text tokens) when
assigning labels to data instances, and write out explanations for the labels
in natural language based on the rationales. NLEs have been shown to capture
human reasoning better, but not as beneficial for natural language inference
(NLI). In this paper, we analyze two primary flaws in the way NLEs are
currently used to train explanation generators for language inference tasks. We
find that the explanation generators do not take into account the variability
inherent in human explanation of labels, and that the current explanation
generation models generate spurious explanations. To overcome these
limitations, we propose a novel framework, LIREx, that incorporates both a
rationale-enabled explanation generator and an instance selector to select only
relevant, plausible NLEs to augment NLI models. When evaluated on the
standardized SNLI data set, LIREx achieved an accuracy of 91.87%, an
improvement of 0.32 over the baseline and matching the best-reported
performance on the data set. It also achieves significantly better performance
than previous studies when transferred to the out-of-domain MultiNLI data set.
Qualitative analysis shows that LIREx generates flexible, faithful, and
relevant NLEs that allow the model to be more robust to spurious explanations.
The code is available at https://github.com/zhaoxy92/LIREx.
