To generate diverse examples, we propose to use a large lexical knowledge base, WordNet, to add perturbations on training examples by replacing some words with their synonyms (e.g., sad and unhappy), neighbor words (e.g., fox and wolf), or super-superior words (e.g., chair and armchair) 3 , as shown in Figure 1.
the output of the generator is a sequence of actions deciding which words should be replaced and their replacements.