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.

Example Input: 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?
Example Output: Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10

Example Input: To help solve the data problem we present Taskmaster-1, a dataset consisting of 13,215 dialogs, including 5,507 spoken and 7,708 written dialogs created with two distinct procedures. Each conversation falls into one of six domains: ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations.  
 Question: Which six domains are covered in the dataset?
Example Output: ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations

Example Input: Our model achieves a 33.33 average of ROUGE-1, ROUGE-2 and ROUGE-L on the CNN/Daily Mail, which is state-of-the-art. 
 Question: What is the ROUGE score of the highest performing model?
Example Output:
33.33 average of ROUGE-1, ROUGE-2 and ROUGE-L