TASK DEFINITION: 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.
PROBLEM: In this paper, we introduce automatic `drunk-texting prediction' as a computational task. Given a tweet, the goal is to automatically identify if it was written by a drunk user.  
 Question: Do the authors equate drunk tweeting with drunk texting? 

SOLUTION: Yes

PROBLEM: Although techniques for constructing this knowledge base are outside the scope of this paper, we briefly mention them. Tables were constructed using a mixture of manual and semi-automatic techniques 
 Question: How is the semi-structured knowledge base created?

SOLUTION: using a mixture of manual and semi-automatic techniques

PROBLEM: Specifically, we design three tasks for seq2seq model pre-training, namely Sentence Reordering (SR), Next Sentence Generation (NSG), and Masked Document Generation (MDG).  
 Question: What is masked document generation?

SOLUTION:
A task for seq2seq model pra-training that recovers a masked document to its original form.