  Grasping the commonsense properties of everyday concepts is an important
prerequisite to language understanding. While contextualised language models
are reportedly capable of predicting such commonsense properties with
human-level accuracy, we argue that such results have been inflated because of
the high similarity between training and test concepts. This means that models
which capture concept similarity can perform well, even if they do not capture
any knowledge of the commonsense properties themselves. In settings where there
is no overlap between the properties that are considered during training and
testing, we find that the empirical performance of standard language models
drops dramatically. To address this, we study the possibility of fine-tuning
language models to explicitly model concepts and their properties. In
particular, we train separate concept and property encoders on two types of
readily available data: extracted hyponym-hypernym pairs and generic sentences.
Our experimental results show that the resulting encoders allow us to predict
commonsense properties with much higher accuracy than is possible by directly
fine-tuning language models. We also present experimental results for the
related task of unsupervised hypernym discovery.
