  Prior work in standardized science exams requires support from large text
corpus, such as targeted science corpus fromWikipedia or SimpleWikipedia.
However, retrieving knowledge from the large corpus is time-consuming and
questions embedded in complex semantic representation may interfere with
retrieval. Inspired by the dual process theory in cognitive science, we propose
a MetaQA framework, where system 1 is an intuitive meta-classifier and system 2
is a reasoning module. Specifically, our method based on meta-learning method
and large language model BERT, which can efficiently solve science problems by
learning from related example questions without relying on external knowledge
bases. We evaluate our method on AI2 Reasoning Challenge (ARC), and the
experimental results show that meta-classifier yields considerable
classification performance on emerging question types. The information provided
by meta-classifier significantly improves the accuracy of reasoning module from
46.6% to 64.2%, which has a competitive advantage over retrieval-based QA
methods.
