Semantic Categorization of Social Knowledge for Commonsense Question Answering (2021)

Abstract: Large pre-trained language models (PLMs) have led to great success on various commonsense question answering (QA) tasks in an end-to-end fashion. However, little attention has been paid to what commonsense knowledge is needed to deeply characterize these QA tasks. In this work, we proposed to categorize the semantics needed for these tasks using the SocialIQA as an example. Building upon our labeled social knowledge categories dataset on top of SocialIQA, we further train neural QA models to incorporate such social knowledge categories and relation information from a knowledge base. Unlike previous work, we observe our models with semantic categorizations of social knowledge can achieve comparable performance with a relatively simple model and smaller size compared to other complex approaches.

## introduction recently, large pre-trained language models (plms) @xcite @xcite have been widely used on various commonsense qa tasks such as commonsenseqa @xcite , socialiqa @xcite , and @xcite ; @xcite ; @xcite ; @xcite ; @xcite . one line of work @xcite improved the performances of these qa tasks by aggregating more qa data and using even bigger plm t5 @xcite . other line of work tried to supplement the question context with retrieval of related knowledge from external knowledge bases (kb), or re-trained plms under the guidance of kbs @xcite @xcite . however, very little past research has paid attention to the specific question/context knowledge types that are needed for these commonsense qa tasks. therefore, in this paper, we go deeper into ## methodology this section presents two approaches to model the underlying semantics and knowledge of the so-cialiqa task, together with a simple yet effective method that leverages these knowledge types to improve qa models. ## conclusion in this work, using the socialiqa task as an example, we integrate two different knowledge types into the qa model training: one based on question relations, and the other is our own defined social knowledge category. experimental results demonstrated that incorporating semantic categorizations of social knowledge into qa models helps boost performances on the social commonsense qa task. the proposed simply ways of incorporating knowledge into the model also achieved comparable performances to these much complicated models.