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.
@article{arxiv.2105.05737,
title = {Encoding Explanatory Knowledge for Zero-shot Science Question Answering},
author = {Zili Zhou and Marco Valentino and Donal Landers and Andre Freitas},
journal= {arXiv preprint arXiv:2105.05737},
year = {2021}
}