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.

[EX 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.
[EX A]: How much data is needed to train the task-specific encoder?

[EX Q]: In the future, we aim to use the corpus for annotation projects such as parts-of-speech tagging, named entity recognition. Furthermore, the generated word embeddings will be utilized for the automatic construction of Sindhi WordNet.
[EX A]: Are trained word embeddings used for any other NLP task?

[EX Q]: As the baseline, we simply judge the input token as IOCs on the basis of the spelling features described in BIBREF12 .
[EX A]:
What is used a baseline?