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.

Q: We introduce a new model for difficulty prediction combining learned representations induced via a pre-trained 'universal' sentence encoder BIBREF6 , and a sentence encoder learned from scratch for this task. In total, this resulted in 57,505 and 2,428 sentences in the train and test set abstracts, respectively.

A: How much data is needed to train the task-specific encoder?
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Q: For the labeled data, we use audio from the IEMOCAP dataset, which comes with labels for activation and valence, both measured on a 5-point Likert scale from three distinct annotators.

A: Which multitask annotated corpus is used?
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Q: For sentiment classification, we systematically study the effect of character-level adversarial attacks on two architectures and four different input formats. We also consider the task of paraphrase detection.

A:
What end tasks do they evaluate on?
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