In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

[EX Q]: The median age is 17 for depressed class versus 19 for control class suggesting either likely depressed-user population is younger, or depressed youngsters are more likely to disclose their age for connecting to their peers (social homophily.) Our findings are consistent with the medical literature BIBREF10 as according to BIBREF52 more women than men were given a diagnosis of depression. 
 Question: What insights into the relationship between demographics and mental health are provided?
[EX A]: either likely depressed-user population is younger, or depressed youngsters are more likely to disclose their age more women than men were given a diagnosis of depression

[EX Q]: For MSA, we acquired the diacritized corpus that was used to train the RDI BIBREF7 diacritizer and the Farasa diacritizer BIBREF31. The corpus contains 9.7M tokens with approximately 194K unique surface forms (excluding numbers and punctuation marks). For testing, we used the freely available WikiNews test set BIBREF31, which is composed of 70 MSA WikiNews articles (18,300 tokens) and evenly covers a variety of genres including politics, economics, health, science and technology, sports, arts and culture. For CA, we obtained a large collection of fully diacritized classical texts (2.7M tokens) from a book publisher, and we held-out a small subset of 5,000 sentences (approximately 400k words) for testing. 
 Question: what datasets were used?
[EX A]: diacritized corpus that was used to train the RDI BIBREF7 diacritizer and the Farasa diacritizer BIBREF31 WikiNews test set BIBREF31  large collection of fully diacritized classical texts (2.7M tokens) from a book publisher

[EX Q]: The release of the FEVER fact extraction and verification dataset BIBREF0 provides a large-scale challenge that tests a combination of retrieval and textual entailment capabilities. To verify a claim in the dataset as supported, refuted, or undecided, a system must retrieve relevant articles and sentences from Wikipedia. Then it must decide whether each of those sentences, or some combination of them, entails or refutes the claim, which is an entailment problem. Systems are evaluated on the accuracy of the claim predictions, with credit only given when correct evidence is submitted. As entailment data, premises in FEVER data differ substantially from those in the image caption data used as the basis for the Stanford Natural Language Inference (SNLI) BIBREF1 dataset. Sentences are longer (31 compared to 14 words on average), vocabulary is more abstract, and the prevalence of named entities and out-of-vocabulary terms is higher. The retrieval aspect of FEVER is not straightforward either. A claim may have small word overlap with the relevant evidence, especially if the claim is refuted by the evidence. 
 Question: What is the FEVER task?
[EX A]:
tests a combination of retrieval and textual entailment capabilities. To verify a claim in the dataset as supported, refuted, or undecided, a system must retrieve relevant articles and sentences from Wikipedia. Then it must decide whether each of those sentences, or some combination of them, entails or refutes the claim, which is an entailment problem