HIGHLIGHTS
- who: Explainable Multi-hop Inference and collaborators from the DepartmentUniversity of have published the paper: Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference, in the Journal: (JOURNAL)
- what: Specifically, the authors propose a methodology to transform existing constraints into differentiable convex optimization layers and subsequently integrate them with pre-trained sentence embeddings based on Transformers (Reimers et_al, 2019). The authors demonstrate that Diff-Explainer is more robust to distracting information in addressing multi-hop inference when compared to Transformer-based models. TableILP (Khashabi et_al, 2016) is one of the earliest Hybrid Reasoning with . . .
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