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Found 17 papers, 7 papers with code
Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks
Recent advances in reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text.
Unification-based Reconstruction of Multi-hop Explanations for Science Questions
This paper presents a novel framework for reconstructing multi-hop explanations in science Question Answering (QA).
Case-Based Abductive Natural Language Inference
Most of the contemporary approaches for multi-hop Natural Language Inference (NLI) construct explanations considering each test case in isolation.
A Survey on Explainability in Machine Reading Comprehension
This paper presents a systematic review of benchmarks and approaches for explainability in Machine Reading Comprehension (MRC).
ExplanationLP: Abductive Reasoning for Explainable Science Question Answering
We propose a novel approach for answering and explaining multiple-choice science questions by reasoning on grounding and abstract inference chains.
Does My Representation Capture X? Probe-Ably
Probing (or diagnostic classification) has become a popular strategy for investigating whether a given set of intermediate features is present in the representations of neural models.
Switching Contexts: Transportability Measures for NLP
This paper explores the topic of transportability, as a sub-area of generalisability.
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference
This paper presents Diff-Explainer, the first hybrid framework for explainable multi-hop inference that integrates explicit constraints with neural architectures through differentiable convex optimization.
Supporting Context Monotonicity Abstractions in Neural NLI Models
Natural language contexts display logical regularities with respect to substitutions of related concepts: these are captured in a functional order-theoretic property called monotonicity.
Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regeneration
Regenerating natural language explanations in the scientific domain has been proposed as a benchmark to evaluate complex multi-hop and explainable inference.
Going Beyond Approximation: Encoding Constraints for Explainable Multi-hop Inference via Differentiable Combinatorial Solvers
Integer Linear Programming (ILP) provides a viable mechanism to encode explicit and controllable assumptions about explainable multi-hop inference with natural language.
A Differentiable Integer Linear Programming Solver for Explanation-Based Natural Language Inference
Integer Linear Programming (ILP) has been proposed as a formalism for encoding precise structural and semantic constraints for Natural Language Inference (NLI).
TextGraphs 2021 Shared Task on Multi-Hop Inference for Explanation Regeneration
While previous editions of this shared task aimed to evaluate explanatory completeness – finding a set of facts that form a complete inference chain, without gaps, to arrive from question to correct answer, this 2021 instantiation concentrates on the subtask of determining relevance in large multi-hop explanations.
TextGraphs 2022 Shared Task on Natural Language Premise Selection
In this summary paper, we present the results of the 1st edition of the NLPS task, providing a description of the evaluation data, and the participating systems.
To be or not to be an Integer? Encoding Variables for Mathematical Text
The application of Natural Language Inference (NLI) methods over large textual corpora can facilitate scientific discovery, reducing the gap between current research and the available large-scale scientific knowledge.
