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 Input:
 News Articles
Our dataset source of news articles is described in BIBREF2. This dataset was built from two different sources, for the trusted news (real news) they sampled news articles from the English Gigaword corpus. For the false news, they collected articles from seven different unreliable news sites. Twitter
For this dataset, we rely on a list of several Twitter accounts for each type of false information from BIBREF6. This list was created based on public resources that annotated suspicious Twitter accounts. The authors in BIBREF6 have built a dataset by collecting tweets from these accounts and they made it available.  
 Question: What datasets did they use?

Ex Output:
News Articles Twitter


Ex Input:
First we are comparing BiLSTMBIBREF14, BiLSTM+CNNBIBREF20, BiLSTM+CRFBIBREF1, BiLSTM+CNN+CRFBIBREF2 models with CNN modelBIBREF0 and Stanford CRF modelBIBREF21. 
 Question: What is the baseline?

Ex Output:
CNN modelBIBREF0 Stanford CRF modelBIBREF21


Ex Input:
Following these methods, we evaluate NCEL on the following five datasets: (1) CoNLL-YAGO BIBREF22 : the CoNLL 2003 shared task including testa of 4791 mentions in 216 documents, and testb of 4485 mentions in 213 documents. (2) TAC2010 BIBREF39 : constructed for the Text Analysis Conference that comprises 676 mentions in 352 documents for testing. (3) ACE2004 BIBREF23 : a subset of ACE2004 co-reference documents including 248 mentions in 35 documents, which is annotated by Amazon Mechanical Turk. (4) AQUAINT BIBREF40 : 50 news articles including 699 mentions from three different news agencies. (5) WW BIBREF19 : a new benchmark with balanced prior distributions of mentions, leading to a hard case of disambiguation. It has 6374 mentions in 310 documents automatically extracted from Wikipedia. 
 Question: Which datasets do they use?

Ex Output:

CoNLL-YAGO TAC2010 ACE2004 AQUAINT WW