e-SNLI: Natural Language Inference with Natural Language Explanations

Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, Phil Blunsom

Abstract:

In order for machine learning to garner widespread public adoption, models must
be able to provide interpretable and robust explanations for their decisions, as
well as learn from human-provided explanations at train time. In this work, we
extend the Stanford Natural Language Inference dataset with an additional layer of
human-annotated natural language explanations of the entailment relations. We further implement models that incorporate these explanations into their training process and output them at test time. We show how our corpus of explanations,
which we call e-SNLI, can be used for various goals, such as obtaining full sentence
justifications of a model’s decisions, improving universal sentence representations
and transferring to out-of-domain NLI datasets. Our dataset1 thus opens up a range
of research directions for using natural language explanations, both for improving
models and for asserting their trust.