  Inferring commonsense knowledge is a key challenge in natural language
processing, but due to the sparsity of training data, previous work has shown
that supervised methods for commonsense knowledge mining underperform when
evaluated on novel data. In this work, we develop a method for generating
commonsense knowledge using a large, pre-trained bidirectional language model.
By transforming relational triples into masked sentences, we can use this model
to rank a triple's validity by the estimated pointwise mutual information
between the two entities. Since we do not update the weights of the
bidirectional model, our approach is not biased by the coverage of any one
commonsense knowledge base. Though this method performs worse on a test set
than models explicitly trained on a corresponding training set, it outperforms
these methods when mining commonsense knowledge from new sources, suggesting
that unsupervised techniques may generalize better than current supervised
approaches.

Computation and Language;Artificial Intelligence;Machine Learning