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.

Example Input: 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?
Example Output: by converting human advice to first-order logic format and use as an input to calculate gradient

Example Input: In this section, we introduce an invertibility condition for our neural projector to tackle the optimization challenge. Specifically, we constrain our neural projector with two requirements: (1) INLINEFORM0 and (2) INLINEFORM1 exists.  
 Question: What is the invertibility condition?
Example Output: The neural projector must be invertible.

Example Input: First, we simply calculate the average unique predictions produced by both INLINEFORM0 and INLINEFORM1 in experiments shown in Section SECREF36 . 
 Question: What two metrics are proposed?
Example Output:
average unique predictions randomly sample 2000 decoder hidden states at INLINEFORM4 steps following a delimiter ( INLINEFORM5 ) and apply an unsupervised clustering method (t-SNE BIBREF35 )