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Currently, contextualized word representations are learned by intricateneural network models, such as masked neural language models (MNLMs). The newrepresentations significantly enhanced the performance in automated questionanswering by reading paragraphs. However, identifying the detailed knowledgetrained in the MNLMs is difficult owing to numerous and intermingledparameters. This paper provides empirical but insightful analyses on thepretrained MNLMs with respect to common sense knowledge. First, we propose atest that measures what types of common sense knowledge do pretrained MNLMsunderstand. From the test, we observed that MNLMs partially understand varioustypes of common sense knowledge but do not accurately understand the semanticmeaning of relations. In addition, based on the difficulty of thequestion-answering task problems, we observed that pretrained MLM-based modelsare still vulnerable to problems that require common sense knowledge. We alsoexperimentally demonstrated that we can elevate existing MNLM-based models bycombining knowledge from an external common sense repository.

Why Do Masked Neural Language Models Still Need Common Sense Knowledge?