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.

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?
 current model

Macaw also supports Wizard of Oz studies or intermediary-based information seeking studies. The architecture of Macaw for such setup is presented in FIGREF16. As shown in the figure, the seeker interacts with a real conversational interface that supports multi-modal and mixed-initiative interactions in multiple devices. The intermediary (or the wizard) receives the seeker's message and performs different information seeking actions with Macaw. All seeker-intermediary and intermediary-system interactions will be logged for further analysis. 
 Question: What is a wizard of oz setup?
seeker interacts with a real conversational interface intermediary (or the wizard) receives the seeker's message and performs different information seeking actions

We then propose a group of new interesting tasks, such as analogy query and analogy browsing, and discuss how they can be used in modern digital libraries. 
 Question: What new interesting tasks can be solved based on the uncanny semantic structures of the embedding space?

 analogy query analogy browsing

