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A fundamental ability of humans is to utilize commonsense knowledge inlanguage understanding and question answering. In recent years, manyknowledge-enhanced Commonsense Question Answering (CQA) approaches have beenproposed. However, it remains unclear: (1) How far can we get by exploitingexternal knowledge for CQA? (2) How much potential of knowledge has beenexploited in current CQA models? (3) Which are the most promising directionsfor future CQA? To answer these questions, we benchmark knowledge-enhanced CQAby conducting extensive experiments on multiple standard CQA datasets using asimple and effective knowledge-to-text transformation framework. Experimentsshow that: (1) Our knowledge-to-text framework is effective and achievesstate-of-the-art performance on CommonsenseQA dataset, providing a simple andstrong knowledge-enhanced baseline for CQA; (2) The potential of knowledge isstill far from being fully exploited in CQA -- there is a significantperformance gap from current models to our models with golden knowledge; and(3) Context-sensitive knowledge selection, heterogeneous knowledgeexploitation, and commonsense-rich language models are promising CQAdirections.

Benchmarking Knowledge-Enhanced Commonsense Question Answering via Knowledge-to-Text Transformation