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.

[EX Q]: For de-identification tasks, the three metrics we will use to evaluate the performance of our architecture are Precision, Recall and INLINEFORM0 score as defined below. 
 Question: What evaluation metrics do they use?
[EX A]: Precision, Recall and INLINEFORM0 score

[EX Q]: The only remaining question is what makes two environments similar enough to infer the existence of a common category. There is, again, a large literature on this question (including the aforementioned language modeling, unsupervised parsing, and alignment work), but in the current work we will make use of a very simple criterion: fragments are interchangeable if they occur in at least one lexical environment that is exactly the same. 
 Question: How do they determine similar environments for fragments in their data augmentation scheme?
[EX A]: fragments are interchangeable if they occur in at least one lexical environment that is exactly the same

[EX Q]: Training was accomplished using the Kaldi Babel receipe using 198 hours of data in 10 languages (Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese) from GlobalPhone. 
 Question: What languages are considered?
[EX A]:
Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese