  There has been recently a growing interest in studying adversarial examples
on natural language models in the black-box setting. These methods attack
natural language classifiers by perturbing certain important words until the
classifier label is changed. In order to find these important words, these
methods rank all words by importance by querying the target model word by word
for each input sentence, resulting in high query inefficiency. A new
interesting approach was introduced that addresses this problem through
interpretable learning to learn the word ranking instead of previous expensive
search. The main advantage of using this approach is that it achieves
comparable attack rates to the state-of-the-art methods, yet faster and with
fewer queries, where fewer queries are desirable to avoid suspicion towards the
attacking agent. Nonetheless, this approach sacrificed the useful information
that could be leveraged from the target classifier for that sake of query
efficiency. In this paper we study the effect of leveraging the target model
outputs and data on both attack rates and average number of queries, and we
show that both can be improved, with a limited overhead of additional queries.
