Abara Integer Linear Programming Model
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%20has%20been%20proposed%20as%20a%20formalism%20for%20encoding%20precise%20structural%20and%20semantic%20constraints%20for%20Natural%20Language%20Inference%20(NLI).%20However%2C%20traditional%20ILP%20frameworks%20are%20non-differentiable%2C%20posing%20critical%20challenges%20for%20the%20integration%20of%20continuous%20language%20representations%20based%20on%20deep%20learning.%20In%20this%20paper%2C%20we%20introduce%20a%20novel%20approach%2C%20named%20Diff-Comb%20Explainer%2C%20a%20neuro-symbolic%20architecture%20for%20explanation-based%20NLI%20based%20on%20Differentiable%20BlackBox%20Combinatorial%20Solvers%20(DBCS).%20Differently%20from%20existing%20neuro-symbolic%20solvers%2C%20Diff-Comb%20Explainer%20does%20not%20necessitate%20a%20continuous%20relaxation%20of%20the%20semantic%20constraints%2C%20enabling%20a%20direct%2C%20more%20precise%2C%20and%20efficient%20incorporation%20of%20neural%20representations%20into%20the%20ILP%20formulation.%20Our%20experiments%20demonstrate%20that%20Diff-Comb%20Explainer%20achieves%20superior%20performance%20when%20compared%20to%20conventional%20ILP%20solvers%2C%20neuro-symbolic%20black-box%20solvers%2C%20and%20Transformer-based%20encoders.%20Moreover%2C%20a%20deeper%20analysis%20reveals%20that%20Diff-Comb%20Explainer%20can%20significantly%20improve%20the%20precision%2C%20consistency%2C%20and%20faithfulness%20of%20the%20constructed%20explanations%2C%20opening%20new%20opportunities%20for%20research%20on%20neuro-symbolic%20architectures%20for%20explainable%20and%20transparent%20NLI%20in%20complex%20domains.)
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