Before revision:  We find that a BERT model with fine-tuning is strikingly successful at the hard generalization tasks we pose using this dataset, and our active manipulations help us to understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations.
After revision:  We find that a BERT model with fine-tuning is strikingly successful at the hard generalization tasks we pose using this dataset, and our active manipulations of model-internal vectors help us understand why: despite the densely interconnected nature of the BERT architecture, the learned model embeds modular, general theories of lexical entailment relations.