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: To fine-tune BERT, we used a proprietary corpus that consists of hundreds of thousands of legal agreements. 
 Question: How big is dataset used for fine-tuning BERT?
Example Output: hundreds of thousands of legal agreements

Example Input: The second, Continuous Bag-of-Words (CBOW), the model is given the sequence of words without the middle one and attempts to predict this omitted word.  
 Question: Which word embeddings are analysed?
Example Output: Continuous Bag-of-Words (CBOW)

Example Input: Our first baseline is ROUGE-L BIBREF1 , since it is the most commonly used metric for compression tasks. We compare to the best n-gram-overlap metrics from toutanova2016dataset; We further compare to the negative LM cross-entropy, i.e., the log-probability which is only normalized by sentence length. Our next baseline is perplexity, which corresponds to the exponentiated cross-entropy: Due to its popularity, we also performed initial experiments with BLEU BIBREF17 .  
 Question: Is ROUGE their only baseline?
Example Output:
No

