e-SNLI: Natural language inference with natural language explanations

Conference item

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.

Authors: Camburu, O; Rocktäschel, T; Lukasiewicz, T; Blunsom, P

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Neural Information Processing Systems
• Host title: Advances in Neural Information Processing Systems 31 (NIPS 2018)
• Journal: 32nd Conference on Neural Information Processing Systems (NIPS 2018)
• Volume: 31
• Publication date: 2018-01-01
• Acceptance date: 2018-09-05