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.

Input: Consider Input: Stanford - Twitter Sentiment Corpus (STS Corpus): STS Corpus contains 1,600K training tweets collected by a crawler from BIBREF0 . BIBREF0 constructed a test set manually with 177 negative and 182 positive tweets. The Stanford test set is small. However, it has been widely used in different evaluation tasks BIBREF0 BIBREF5 BIBREF13 .

Sanders - Twitter Sentiment Corpus: This dataset consists of hand-classified tweets collected by using search terms: INLINEFORM0 , #google, #microsoft and #twitter. We construct the dataset as BIBREF14 for binary classification.

Health Care Reform (HCR): This dataset was constructed by crawling tweets containing the hashtag #hcr BIBREF15 . Task is to predict positive/negative tweets BIBREF14 . 
 Question: Which datasets did they use?

Output: Stanford - Twitter Sentiment Corpus (STS Corpus) Sanders - Twitter Sentiment Corpus Health Care Reform (HCR)


Input: Consider Input: Introduced by BIBREF16 , in this dataset, the input query is the concatenation of a Wikipedia article title with the title of one of its section. The relevant passages are the paragraphs within that section. The corpus consists of all of the English Wikipedia paragraphs, except the abstracts. The released dataset has five predefined folds, and we use the first four as a training set (approximately 3M queries), and the remaining as a validation set (approximately 700k queries). The test set is the same one used to evaluate the submissions to TREC-CAR 2017 (approx. 1,800 queries). 
 Question: What is the TREC-CAR dataset?

Output: in this dataset, the input query is the concatenation of a Wikipedia article title with the title of one of its section. The relevant passages are the paragraphs within that section


Input: Consider Input: In addition, in order to estimate annotation reliability and provide for better evaluation, every question in the test set is answered by at least two additional experts. 
 Question: Are the answers double (and not triple) annotated?


Output: Yes