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Recently several datasets have been proposed to encourage research inQuestion Answering domains where commonsense knowledge is expected to play animportant role. Recent language models such as ROBERTA, BERT and GPT that havebeen pre-trained on Wikipedia articles and books have shown reasonableperformance with little fine-tuning on several such Multiple ChoiceQuestion-Answering (MCQ) datasets. Our goal in this work is to develop methodsto incorporate additional (commonsense) knowledge into language model-basedapproaches for better question-answering in such domains. In this work, wefirst categorize external knowledge sources, and show performance does improveon using such sources. We then explore three different strategies for knowledgeincorporation and four different models for question-answering using externalcommonsense knowledge. We analyze our predictions to explore the scope offurther improvements.

How Additional Knowledge can Improve Natural Language Commonsense Question Answering?