instruction:
In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
question:
Thus, to limit the number of different inputs to the classifier, we wish to reduce the number of distinct word recognition outputs that an attacker can induce, not just the number of words on which the model is “fooled”. We denote this property of a model as its sensitivity.
answer:
What does the "sensitivity" quantity denote?


question:
We adopt a DNN-based acoustic model BIBREF0 with 11 hidden layers and the alignment used to train the model is derived from a HMM-GMM model trained with SAT criterion.
answer:
What are the deep learning architectures used in the task?


question:
 Last, we evaluate our approaches in 9 commonly used text classification datasets. We evaluate our methods on several commonly used datasets whose themes range from sentiment, web-page, science to medical and healthcare.
answer:

What NLP tasks do they consider?