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]: We use Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10 as baselines and compare the results with our different fine-tuning strategies using pre-trained BERTbase model.  
 Question: What existing approaches do they compare to?
[EX A]: Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10

[EX Q]:  For a fair comparison with previous work, we train the baseline MT system on the data released by BIBREF11.  
 Question: what was the baseline?
[EX A]:  MT system on the data released by BIBREF11

[EX Q]: This way, we can label 77 new pro-Russian edges by looking at 415 tweets, which means that 19% of the candidates are hits. For the pro-Ukrainian class, we can label 110 new edges by looking at 611 tweets (18% hits). Hence even though the quality of the classifier predictions is too low to be integrated into the network analysis right away, the classifier drastically facilitates the annotation process for human annotators compared to annotating unfiltered tweets (from the original labels we infer that for unfiltered tweets, only 6% are hits for the pro-Russian class, and 11% for the pro-Ukrainian class). 
 Question: How can the classifier facilitate the annotation task for human annotators?
[EX A]:
quality of the classifier predictions is too low to be integrated into the network analysis right away, the classifier drastically facilitates the annotation process for human annotators compared to annotating unfiltered tweets

