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.

[Q]: We modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions.

Results showed that users who, on average, produced utterances with more words gave significantly higher ratings ($\beta $=0.01, SE=0.002, t=4.79, p$<$0.001)(see Figure 2) and engaged with Gunrock for significantly greater number of turns ($\beta $=1.85, SE=0.05, t=35.58, p$<$0.001) (see Figure 2). These results can be interpreted as evidence for Gunrock's ability to handle complex sentences, where users are not constrained to simple responses to be understood and feel engaged in the conversation – and evidence that individuals are more satisfied with the conversation when they take a more active role, rather than the system dominating the dialog. On the other hand, another interpretation is that users who are more talkative may enjoy talking to the bot in general, and thus give higher ratings in tandem with higher average word counts. 
 Question: How do they correlate user backstory queries to user satisfaction?
[A]: modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions


[Q]: One example illustrates that the mistakes the model makes can be associated with changes in the party policy. 
 Question: Do changes in policies of the political actors account for all of the mistakes the model made?
[A]: Yes


[Q]: We recruited 176 AMT workers to participate in our conceptualization task. 
 Question: What crowdsourcing platform was used?
[A]:
AMT