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]: At each training step, we train the model with a batch of original examples along with adversarial examples with balanced labels. The adversarial examples account for around 10% in a batch. During training, we generate adversarial examples with the current model as the target and update the model parameters with the hybrid batch iteratively. 
 Question: What is discriminator in this generative adversarial setup?
[A]:  current model


[Q]:  Models include both neural nets (e.g. RNNs, CNNs) and standard machine learning tools (e.g. Naive Bayes with Laplace Smoothing, k-clustering, SVM with linear kernel).  
 Question: Which machine learning models are used?
[A]: RNNs CNNs Naive Bayes with Laplace Smoothing k-clustering SVM with linear kernel


[Q]: We first introduce a method that detects bottlenecks in text-games using the overall reward gained and the knowledge graph state. This method freezes the policy used to reach the bottleneck and restarts the training from there on out, additionally conducting a backtracking search to ensure that a sub-optimal policy has not been frozen. The second contribution explore how to leverage knowledge graphs to improve existing exploration algorithms for dealing with combinatorial action-spaces such as Go-Explore BIBREF9.  
 Question: What are the two new strategies?
[A]:
a method that detects bottlenecks in text-games using the overall reward gained and the knowledge graph state to leverage knowledge graphs to improve existing exploration algorithms for dealing with combinatorial action-space