Diff-explainer: differentiable convex optimization for explainable multi-hop inference

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 . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or E-mail:

  If you don't have an account, you can create one here.