Interpretable Proof Generation via Iterative Backward Reasoning (2022.naacl-main)
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| Challenge: | Existing proof generation tasks require reasoning capabilities, but they usually just request for an answer without the reasoning procedure that would make it interpretable. |
| Approach: | They propose an iterative backward reasoning model to solve the proof generation tasks on rule-based Question Answering. |
| Outcome: | The proposed model improves in-domain performance and cross-domain transferability over existing models. |
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