  Recent advances in general purpose pre-trained language models have shown
great potential in commonsense reasoning. However, current works still perform
poorly on standard commonsense reasoning benchmarks including the Com2Sense
Dataset. We argue that this is due to a disconnect with current cutting-edge
machine learning methods. In this work, we aim to bridge the gap by introducing
current ML-based methods to improve general purpose pre-trained language models
in the task of commonsense reasoning. Specifically, we experiment with and
systematically evaluate methods including knowledge transfer, model ensemble,
and introducing an additional pairwise contrastive objective. Our best model
outperforms the strongest previous works by ~15\% absolute gains in Pairwise
Accuracy and ~8.7\% absolute gains in Standard Accuracy.
