A fundamental ability of humans is to utilize commonsense knowledge in
language understanding and question answering. In recent years, many
knowledge-enhanced Commonsense Question Answering (CQA) approaches have been
proposed. However, it remains unclear: (1) How far can we get by exploiting
external knowledge for CQA? (2) How much potential of knowledge has been
exploited in current CQA models? (3) Which are the most promising directions
for future CQA? To answer these questions, we benchmark knowledge-enhanced CQA
by conducting extensive experiments on multiple standard CQA datasets using a
simple and effective knowledge-to-text transformation framework. Experiments
show that: (1) Our knowledge-to-text framework is effective and achieves
state-of-the-art performance on CommonsenseQA dataset, providing a simple and
strong knowledge-enhanced baseline for CQA; (2) The potential of knowledge is
still far from being fully exploited in CQA -- there is a significant
performance gap from current models to our models with golden knowledge; and
(3) Context-sensitive knowledge selection, heterogeneous knowledge
exploitation, and commonsense-rich language models are promising CQA
directions.

研究人员基于知识增强的常识问答模型，在多个标准常识问答数据集上进行了大量实验，发现当前模型的知识潜力还远未被充分挖掘，提出了上下文敏感的知识选择、异构知识利用和基于常识的语言模型是未来常识问答的有效方向。