Scale appears to be the winning recipe in today’s leaderboards. And yet, extreme-scale neural models are still brittle to make errors that are often nonsensical and even counterintuitive. In this talk, I will argue for the importance of knowledge, especially commonsense knowledge, and demonstrate how smaller models developed in academia can still have an edge over larger industry-scale models, if powered with knowledge.

First, I will introduce “symbolic knowledge distillation”, a new framework to distill larger neural language models into smaller commonsense models, which leads to a machine-authored KB that wins, for the first time, over a human-authored KB in all criteria: scale, accuracy, and diversity. Next, I will introduce a new conceptual framework for language-based commonsense moral reasoning, and discuss how we can teach neural language models about complex social norms and human values, so that the machine can reason that “helping a friend” is generally a good thing to do, but “helping a friend spread fake news” is not. Finally, I will discuss an approach to multimodal script knowledge, which leads to new SOTA performances on a dozen leaderboards that require grounded, temporal, and causal commonsense reasoning.