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Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference. (arXiv:2105.03417v2 [cs.CL] UPDATED)
June 23, 2022, 1:12 a.m. | Mokanarangan Thayaparan, Marco Valentino, Deborah Ferreira, Julia Rozanova, André Freitas
cs.CL updates on arXiv.org arxiv.org
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 …
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