  This paper describes N-XKT (Neural encoding based on eXplanatory Knowledge
Transfer), a novel method for the automatic transfer of explanatory knowledge
through neural encoding mechanisms. We demonstrate that N-XKT is able to
improve accuracy and generalization on science Question Answering (QA).
Specifically, by leveraging facts from background explanatory knowledge
corpora, the N-XKT model shows a clear improvement on zero-shot QA.
Furthermore, we show that N-XKT can be fine-tuned on a target QA dataset,
enabling faster convergence and more accurate results. A systematic analysis is
conducted to quantitatively analyze the performance of the N-XKT model and the
impact of different categories of knowledge on the zero-shot generalization
task.
