TASK DEFINITION: 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.
PROBLEM: As indicated in its name, Recurrent Deep Stacking Network stacks and concatenates the outputs of previous frames into the input features of the current frame.

SOLUTION: What does recurrent deep stacking network do?

PROBLEM: oss analysis. To conduct a more thorough evaluation, we further investigate model behaviours in terms of both reconstruc

SOLUTION: How do they evaluate generated text quality?

PROBLEM: In our setup, our starting point is a base model, trained on NLI data. Rather than employing automated adversarial methods, here the model's “adversary” is a human annotator. Given a context (also often called a “premise” in NLI), and a desired target label, we ask the human writer to provide a hypothesis that fools the model into misclassifying the label. One can think of the writer as a “white hat” hacker, trying to identify vulnerabilities in the system. For each human-generated example that is misclassified, we also ask the writer to provide a reason why they believe it was misclassified.

SOLUTION:
Do they use active learning to create their dataset?