Explaining NLP Models via Minimal Contrastive Editing (MiCE) (2021.findings-acl)
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| Challenge: | Cognitive science and philosophy research has shown that human explanations are contrastive . a contrast case plays a key role in modulating what explanations can be given . |
| Approach: | They propose a method for producing contrastive explanations of model predictions . they edit models' outputs to change model outputs, and then edit them to the contrast case . |
| Outcome: | a new method produces contrastive explanations of model predictions in the form of edits . the edits are minimal and fluent, consistent with human contrastive edits. |
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