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: Although techniques for constructing this knowledge base are outside the scope of this paper, we briefly mention them. Tables were constructed using a mixture of manual and semi-automatic techniques 
 Question: How is the semi-structured knowledge base created?
Example Output: using a mixture of manual and semi-automatic techniques

Example Input: Table TABREF31 shows the F-score, the Area Under the ROC Curve (AUC) and the mean accuracy (ACC) of the proposed models when different inputs are available. Fig. FIGREF32 shows the Precision vs Recall plot and the ROC curve (which plots the True Positive Rate vs the False Positive Rate) of the different models. 
 Question: What metrics are used to benchmark the results?
Example Output:
F-score Area Under the ROC Curve (AUC) mean accuracy (ACC) Precision vs Recall plot ROC curve (which plots the True Positive Rate vs the False Positive Rate)