e-SNLI: Natural language inference with natural language explanations
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...
| Main Authors: | , , , |
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| Format: | Conference item |
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Neural Information Processing Systems
2018
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