Inducing Taxonomic Knowledge from Pretrained Transformers

We present a method for inducing taxonomic trees from pretrained transformers. Given a set of input terms, we assign a score for the likelihood that each pair of terms forms a parent-child relation. To produce a tree from pairwise parent-child edge scores, we treat this as a graph optimization problem and output the maximum spanning tree. We train the model by finetuning it on parent-child relations from subtrees of WordNet and test on non-overlapping subtrees. In addition, we incorporate semi-structured definitions from the web to further improve performance. On the task of inducing subtrees of WordNet, the model achieves 66.0 ancestor F_1, a 10.4 point absolute increase over the previous best published result on this task.