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 baseline is used?
Example Output: Waseem and Hovy BIBREF5 Davidson et al. BIBREF9 Waseem et al. BIBREF10

Example Input: We design the word-level refine decoder because this process is similar to the cloze task in BERT's pre-train process, therefore by using the ability of the contextual language model the decoder can generate more fluent and natural sequences. 
 Question: Why masking words in the decoder is helpful?
Example Output: ecause this process is similar to the cloze task in BERT's pre-train process, therefore by using the ability of the contextual language model the decoder can generate more fluent and natural sequences.

Example Input: We crowd-sourced the collection of the dataset on Amazon Mechanical Turk (MTurk). 
 Question: Did the authors use crowdsourcing platforms?
Example Output:
Yes