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.

Input: Consider Input: As in the Sequicity framework, we report entity match rate, BLEU score and Success F1 score.  
 Question: What were the evaluation metrics used?

Output: entity match rate BLEU score Success F1 score


Input: Consider Input: Posts in FBFans dataset are used for this analysis. We calculate the like statistics of each distinct author from these 32,595 posts. 
 Question: What is the size of the Chinese data?

Output: 32,595 posts


Input: Consider Input: In our setup, our starting point is a base model, trained on NLI data. Rather than employing automated adversarial methods, here the model's “adversary” is a human annotator. Given a context (also often called a “premise” in NLI), and a desired target label, we ask the human writer to provide a hypothesis that fools the model into misclassifying the label. One can think of the writer as a “white hat” hacker, trying to identify vulnerabilities in the system. For each human-generated example that is misclassified, we also ask the writer to provide a reason why they believe it was misclassified. 
 Question: Do they use active learning to create their dataset?


Output: Yes