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: Although MCDN doesn't obtain the highest precision, it increases F1-score by 10.2% and 3% compared with the existing best systems $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$. 
 Question: What performance did proposed method achieve, how much better is than previous state-of-the-art?

Output: increases F1-score by 10.2% and 3% compared with the existing best systems $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$


Input: Consider Input: To simplify the task, we set up a binary classification: candidates who have been liked or shortlisted are considered part of the hirable class and others part of the not hirable class. 
 Question: How is "hirability" defined?

Output: candidates who have been liked or shortlisted are considered part of the hirable class


Input: Consider Input: The final code-mixed tweets were forwarded to a group of three annotators who were university students and fluent in both English and Hindi. 
 Question: How many annotators tagged each text?


Output: three