Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference
This work addresses the need for structured explanations in multi-hop question answering, which is incremental as it combines existing ILP solvers and Transformer representations in a novel hybrid framework.
The paper tackled the problem of explainable multi-hop inference in natural language by integrating explicit constraints with neural architectures through differentiable convex optimization, resulting in performance improvements of 8.91% to 13.3% for non-differentiable ILP solvers on scientific and commonsense QA tasks.
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.