A Study of Automatically Acquiring Explanatory Inference Patterns from Corpora of Explanations: Lessons from Elementary Science Exams

Our long term interest is in building inference algorithms capable of answering questions and producing human-readable explanations by aggregating information from multiple sources and knowledge bases. Currently information aggregation (also referred to as “multi-hop inference”) is challenging for more than two facts due to “semantic drift”, or the tendency for natural language inference algorithms to quickly move off-topic when assembling long chains of knowledge. In this paper we explore the possibility of generating large explanations with an average of six facts by automatically extracting common explanatory patterns from a corpus of manually authored elementary science explanations represented as lexically-connected explanation graphs grounded in a semi-structured knowledge base of tables. We empirically demonstrate that there are sufficient common explanatory patterns in this corpus that it is possible in principle to reconstruct unseen explanation graphs by merging multiple explanatory patterns, then adapting and/or adding to their knowledge. This may ultimately provide a mechanism to allow inference algorithms to surpass the two-fact “aggregation horizon” in practice by using common explanatory patterns as constraints to limit the search space during information aggregation.