  Commonsense reasoning is intuitive for humans but has been a long-term
challenge for artificial intelligence (AI). Recent advancements in pretrained
language models have shown promising results on several commonsense benchmark
datasets. However, the reliability and comprehensiveness of these benchmarks
towards assessing model's commonsense reasoning ability remains unclear. To
this end, we introduce a new commonsense reasoning benchmark dataset comprising
natural language true/false statements, with each sample paired with its
complementary counterpart, resulting in 4k sentence pairs. We propose a
pairwise accuracy metric to reliably measure an agent's ability to perform
commonsense reasoning over a given situation. The dataset is crowdsourced and
enhanced with an adversarial model-in-the-loop setup to incentivize challenging
samples. To facilitate a systematic analysis of commonsense capabilities, we
design our dataset along the dimensions of knowledge domains, reasoning
scenarios and numeracy. Experimental results demonstrate that our strongest
baseline (UnifiedQA-3B), after fine-tuning, achieves ~71% standard accuracy and
~51% pairwise accuracy, well below human performance (~95% for both metrics).
The dataset is available at https://github.com/PlusLabNLP/Com2Sense.
