Explanation Regeneration via Multi-Hop ILP Inference over Knowledge Base

摘要

Textgraphs 2020 Workshop organized a shared task on 'Explanation Regeneration' that required reconstructing gold explanations for elementary science questions. This work describes our submission to the task which is based on multiple components: a BERT baseline ranking, an Integer Linear Program (ILP) based re-scoring and a regression model for re-ranking the explanation facts. Our system achieved a Mean Average Precision score of 0.3659.