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Large pre-trained language models (PLMs) have led to great success on variouscommonsense question answering (QA) tasks in an end-to-end fashion. However,little attention has been paid to what commonsense knowledge is needed todeeply characterize these QA tasks. In this work, we proposed to categorize thesemantics needed for these tasks using the SocialIQA as an example. Buildingupon our labeled social knowledge categories dataset on top of SocialIQA, wefurther train neural QA models to incorporate such social knowledge categoriesand relation information from a knowledge base. Unlike previous work, weobserve our models with semantic categorizations of social knowledge canachieve comparable performance with a relatively simple model and smaller sizecompared to other complex approaches.

Semantic Categorization of Social Knowledge for Commonsense Question Answering