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Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference
[article]
2022
arXiv
pre-print
This paper presents Diff-Explainer, the first hybrid framework for explainable multi-hop inference that integrates explicit constraints with neural architectures through differentiable convex optimization. Specifically, Diff-Explainer allows for the fine-tuning of neural representations within a constrained optimization framework to answer and explain multi-hop questions in natural language. To demonstrate the efficacy of the hybrid framework, we combine existing ILP-based solvers for multi-hop
arXiv:2105.03417v2
fatcat:fvh7uo5l2varrdjmebeusmgnte