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e-SNLI: Natural Language Inference with Natural Language Explanations
[article]
2018
arXiv
pre-print
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
arXiv:1812.01193v2
fatcat:leshzirbs5d7rjmuhliimonzvq