  Pretrained language models have demonstrated outstanding performance in many
NLP tasks recently. However, their social intelligence, which requires
commonsense reasoning about the current situation and mental states of others,
is still developing. Towards improving language models' social intelligence, we
focus on the Social IQA dataset, a task requiring social and emotional
commonsense reasoning. Building on top of the pretrained RoBERTa and GPT2
models, we propose several architecture variations and extensions, as well as
leveraging external commonsense corpora, to optimize the model for Social IQA.
Our proposed system achieves competitive results as those top-ranking models on
the leaderboard. This work demonstrates the strengths of pretrained language
models, and provides viable ways to improve their performance for a particular
task.
