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This paper presents a novel framework for reconstructing multi-hopexplanations in science Question Answering (QA). While existing approaches formulti-hop reasoning build explanations considering each question in isolation,we propose a method to leverage explanatory patterns emerging in a corpus ofscientific explanations. Specifically, the framework ranks a set of atomicfacts by integrating lexical relevance with the notion of unification power,estimated analysing explanations for similar questions in the corpus.  An extensive evaluation is performed on the Worldtree corpus, integratingk-NN clustering and Information Retrieval (IR) techniques. We present thefollowing conclusions: (1) The proposed method achieves results competitivewith Transformers, yet being orders of magnitude faster, a feature that makesit scalable to large explanatory corpora (2) The unification-based mechanismhas a key role in reducing semantic drift, contributing to the reconstructionof many hops explanations (6 or more facts) and the ranking of complexinference facts (+12.0 Mean Average Precision) (3) Crucially, the constructedexplanations can support downstream QA models, improving the accuracy of BERTby up to 10% overall.

Unification-based Reconstruction of Multi-hop Explanations for Science Questions