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: 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?

A: Precision, Recall and INLINEFORM0 score
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Q: Our data set contains $3,209$ reviews about 553 different cars from 49 different car manufacturers. 
 Question: How big is dataset of car-speak language?

A: $3,209$ reviews 
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Q: A few sample advice rules in English (these are converted to first-order logic format and given as input to our algorithm) are presented in Table TABREF11 .  We modified the work of Odom et al. odomAIME15,odomAAAI15 to learn RDNs in the presence of advice. The key idea is to explicitly represent advice in calculating gradients. 
 Question: How do they incorporate human advice?

A:
by converting human advice to first-order logic format and use as an input to calculate gradient
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