  We propose a novel approach for answering and explaining multiple-choice
science questions by reasoning on grounding and abstract inference chains. This
paper frames question answering as an abductive reasoning problem, constructing
plausible explanations for each choice and then selecting the candidate with
the best explanation as the final answer. Our system, ExplanationLP, elicits
explanations by constructing a weighted graph of relevant facts for each
candidate answer and extracting the facts that satisfy certain structural and
semantic constraints. To extract the explanations, we employ a linear
programming formalism designed to select the optimal subgraph. The graphs'
weighting function is composed of a set of parameters, which we fine-tune to
optimize answer selection performance. We carry out our experiments on the
WorldTree and ARC-Challenge corpus to empirically demonstrate the following
conclusions: (1) Grounding-Abstract inference chains provides the semantic
control to perform explainable abductive reasoning (2) Efficiency and
robustness in learning with a fewer number of parameters by outperforming
contemporary explainable and transformer-based approaches in a similar setting
(3) Generalisability by outperforming SOTA explainable approaches on general
science question sets.
