Diff-Explainer：可解释多跳推理的可微凸优化
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference
摘要：
本文介绍了Diff-Explainer，第一个混合框架，可解释的多跳推理，集成了显式约束与神经架构，通过可微凸优化。具体来说，Diff-Explainer允许在约束优化框架内微调神经表示，以自然语言回答和解释多跳问题。为了证明混合框架的有效性，我们结合现有的基于ILP的求解器的多跳问答（QA）与基于变换器的表示。对科学和常识QA任务的广泛实证评估表明，在端到端可微框架中集成显式约束可以显着提高不可微ILP求解器的性能（8.91% - 13.3%）。此外，额外的分析表明，Diff-Explainer能够实现强大的性能相比，独立的Transformers和以前的多跳方法，同时仍然提供结构化的解释，以支持其预测。
英文摘要：
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 integration of explicit constraints in an end-to-end differentiable framework can significantly improve the performance of non-differentiable ILP solvers (8.91% - 13.3%). Moreover, additional analysis reveals that Diff-Explainer is able to achieve strong performance when compared to standalone Transformers and previous multi-hop approaches while still providing structured explanations in support of its predictions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
