instruction:
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.
question:
 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?
answer:
News Articles Twitter


question:
We draw on a recently released corpus of state speeches delivered during the annual UN General Debate that provides the first dataset of textual output from states that is recorded at regular time-series intervals and includes a sample of all countries that deliver speeches BIBREF11 .  
 Question: Which dataset do they use?
answer:
corpus of state speeches delivered during the annual UN General Debate


question:
The final results for our F1-score from this classification technique are 0.52 and 0.31 for the single and multi-turn data respectively.

We compare our results for both the single-turn and multi-turn experiments to the accuracies on the test data based off the BLEU score. 
 Question: which existing metrics do they compare with?
answer:

F1-score BLEU score