Recent large neural models have shown impressive performance on various data modalities, including natural language, vision, programming language and molecules. However, they still have surprising deficiency (near-random performance) in acquiring certain types of knowledge such as structured knowledge and action knowledge In this talk I propose a two-way knowledge acquisition framework to make symbolic and neural learning approaches mutually enhance each other. In the first stage, we will elicit…