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Introducing common sense to natural language understanding systems hasreceived increasing research attention. It remains a fundamental question onhow to evaluate whether a system has a sense making capability. Existingbenchmarks measures commonsense knowledge indirectly and without explanation.In this paper, we release a benchmark to directly test whether a system candifferentiate natural language statements that make sense from those that donot make sense. In addition, a system is asked to identify the most crucialreason why a statement does not make sense. We evaluate models trained overlarge-scale language modeling tasks as well as human performance, showing thatthere are different challenges for system sense making.

Does It Make Sense? And Why? A Pilot Study for Sense Making and Explanation