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]: Fig. FIGREF4 shows the distributions of seven question types grouped deterministically from the lexicons. 
 Question: How many question types do they find in the datasets analyzed?
[A]: seven 


[Q]: In our methodology to design new evaluation metrics for comparing reference summaries/translations to hypothesis ones, we established first-principles criteria on what a good evaluator should do. The first one is that it should be highly correlated with human judgement of similarity. The second one is that it should be able to distinguish sentences which are in logical contradiction, logically unrelated or in logical agreement. The third one is that a robust evaluator should also be able to identify unintelligible sentences. The last criteria is that a good evaluation metric should not give high scores to semantically distant sentences and low scores to semantically related sentences. 
 Question: What is the criteria for a good metric?
[A]: The first one is that it should be highly correlated with human judgement of similarity. The second one is that it should be able to distinguish sentences which are in logical contradiction, logically unrelated or in logical agreement. The third one is that a robust evaluator should also be able to identify unintelligible sentences. The last criteria is that a good evaluation metric should not give high scores to semantically distant sentences and low scores to semantically related sentences.


[Q]: Besides the Baseline and Oracle, where only ASR 1-best hypothesis is considered, we also perform experiments to utilize ASR $n$-best hypotheses during evaluation. 
 Question: What are the series of simple models?
[A]:
perform experiments to utilize ASR $n$-best hypotheses during evaluation