Explainable Inference Over Grounding-Abstract Chains for Science Questions

We propose an explainable inference approach for science questions by reasoning on grounding and abstract inference chains. This paper frames question answering as a natural language abductive reasoning problem, constructing plausible explanations for each candidate answer and then selecting the candidate with the best explanation as the final answer. Our method, ExplanationLP, elicits explanations by constructing a weighted graph of relevant facts for each candidate answer and employs a linear programming formalism designed to select the optimal subgraph of explanatory facts. The graphs' weighting function is composed of a set of parameters targeting relevance, cohesion and diversity, which we finetune for answer selection via Bayesian Optimisation. We carry out our experiments on the WorldTree and ARC-Challenge datasets to empirically demonstrate the following contributions: (1) ExplanationLP obtains strong performance when compared to transformer-based and multi-hop approaches despite having a significantly lower number of parameters; (2) We show that our model is able to generate plausible explanations for answer prediction; (3) Our model demonstrates better robustness towards semantic drift when compared to transformerbased and multi-hop approaches.