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


[Q]: Experiments on two publicly available datasets (Camrest BIBREF11 and InCar Assistant BIBREF6) confirm the effectiveness of the KB-retriever. 
 Question: Which dialog datasets did they experiment with?
[A]: Camrest InCar Assistant


[Q]: We approached the first and second challenges by using a Bayesian approach to learn which terms were associated with events, regardless of whether they are standard language, acronyms, or even a made-up word, so long as they match the events of interest. The third and fourth challenges are approached by using word-pairs, where we extract all the pairs of co-occurring words within each tweet. To find the words most associated with events, we search for the words that achieve the highest number of spikes matching the days of events.  
 Question: How are the keywords associated with events such as protests selected?
[A]:
By using a Bayesian approach  and by using word-pairs, where they extract all the pairs of co-occurring words within each tweet.  They search for the words that achieve the highest number of spikes matching the days of events.