1 citations · 2 across the 4 of their papers we have counts for
5 papers
A Differentiable Integer Linear Programming Solver for Explanation-Based Natural Language Inference
Mokanarangan Thayaparan, Marco Valentino, André Freitas
Integer Linear Programming (ILP) has been proposed as a formalism for encoding precise structural and semantic constraints for Natural Language Inference (NLI). However, traditiona…
Going Beyond Approximation: Encoding Constraints for Explainable Multi-hop Inference via Differentiable Combinatorial Solvers
Mokanarangan Thayaparan, Marco Valentino, André Freitas
Integer Linear Programming (ILP) provides a viable mechanism to encode explicit and controllable assumptions about explainable multi-hop inference with natural language. However, a…
Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regeneration
Marco Valentino, Mokanarangan Thayaparan, Deborah Ferreira +1
Regenerating natural language explanations in the scientific domain has been proposed as a benchmark to evaluate complex multi-hop and explainable inference. In this context, large…
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference
Mokanarangan Thayaparan, Marco Valentino, Deborah Ferreira +2
This paper presents Diff-Explainer, the first hybrid framework for explainable multi-hop inference that integrates explicit constraints with neural architectures through differenti…
Case-Based Abductive Natural Language Inference
Marco Valentino, Mokanarangan Thayaparan, André Freitas
Most of the contemporary approaches for multi-hop Natural Language Inference (NLI) construct explanations considering each test case in isolation. However, this paradigm is known t…