Integer Linear Programming Solver
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 has been proposed as a formalism for encoding precise structural and semantic constraints for Natural Language Inference (NLI). However%2C traditional ILP frameworks are non-differentiable%2C posing critical challenges for the integration of continuous language representations based on deep learning. In this paper%2C we introduce a novel approach%2C named Diff-Comb Explainer%2C a neuro-symbolic architecture for explanation-based NLI based on Differentiable BlackBox Combinatorial Solvers (DBCS). Differently from existing neuro-symbolic solvers%2C Diff-Comb Explainer does not necessitate a continuous relaxation of the semantic constraints%2C enabling a direct%2C more precise%2C and efficient incorporation of neural representations into the ILP formulation. Our experiments demonstrate that Diff-Comb Explainer achieves superior performance when compared to conventional ILP solvers%2C neuro-symbolic black-box solvers%2C and Transformer-based encoders. Moreover%2C a deeper analysis reveals that Diff-Comb Explainer can significantly improve the precision%2C consistency%2C and faithfulness of the constructed explanations%2C opening new opportunities for research on neuro-symbolic architectures for explainable and transparent NLI in complex domains. "Integer Linear Programming Solver")
Integer Linear Programming Solver
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