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.

The Y-axis in the figure is the success rate of the agent (measured in terms of number of dialogs that resulted in launching a skill divided by total number of dialogs), and the X-axis is the number of learning steps.  
 Question: How did they measure effectiveness?
number of dialogs that resulted in launching a skill divided by total number of dialogs

Although MCDN doesn't obtain the highest precision, it increases F1-score by 10.2% and 3% compared with the existing best systems $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$. 
 Question: What performance did proposed method achieve, how much better is than previous state-of-the-art?
increases F1-score by 10.2% and 3% compared with the existing best systems $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$

To compare our neural models with the traditional approaches, we experimented with a number of existing models including: Support Vector Machine (SVM), a discriminative max-margin model; Logistic Regression (LR), a discriminative probabilistic model; and Random Forest (RF), an ensemble model of decision trees. 
 Question: what was their baseline comparison?

Support Vector Machine (SVM) Logistic Regression (LR) Random Forest (RF)