Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference. (arXiv:2105.03417v2 [cs.CL] UPDATED)

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 Question Answering (QA) with
Transformer-based representations. An extensive empirical evaluation on
scientific and commonsense QA tasks demonstrates that the …

arxiv diff explainer inference optimization