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Many contextualized word representations are now learned by intricate neuralnetwork models, such as masked neural language models (MNLMs) which are made upof huge neural network structures and trained to restore the masked text. Suchrepresentations demonstrate superhuman performance in some readingcomprehension (RC) tasks which extract a proper answer in the context given aquestion. However, identifying the detailed knowledge trained in MNLMs ischallenging owing to numerous and intermingled model parameters. This paperprovides new insights and empirical analyses on commonsense knowledge includedin pretrained MNLMs. First, we use a diagnostic test that evaluates whethercommonsense knowledge is properly trained in MNLMs. We observe that a largeproportion of commonsense knowledge is not appropriately trained in MNLMs andMNLMs do not often understand the semantic meaning of relations accurately. Inaddition, we find that the MNLM-based RC models are still vulnerable tosemantic variations that require commonsense knowledge. Finally, we discoverthe fundamental reason why some knowledge is not trained. We further suggestthat utilizing an external commonsense knowledge repository can be an effectivesolution. We exemplify the possibility to overcome the limitations of theMNLM-based RC models by enriching text with the required knowledge from anexternal commonsense knowledge repository in controlled experiments.

Why Do Neural Language Models Still Need Commonsense Knowledge to Handle Semantic Variations in Question Answering?