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: 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?

A: seeker interacts with a real conversational interface intermediary (or the wizard) receives the seeker's message and performs different information seeking actions
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Q: The input for both tasks consists of news articles in free-text format, collected from 36 propagandist and 12 non-propagandist news outlets and then annotated by professional annotators.  
 Question: What dataset was used?

A:  news articles in free-text format
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Q: This metric is used to evaluate the accuracy of dialogue belief tracker BIBREF1.

APRA: Action Per-Response Accuracy (APRA) evaluates the per-turn accuracy of the dialogue actions generated by dialogue policy maker. For baselines, APRA evaluates the classification accuracy of the dialogue policy maker. But our model actually generates each individual token of actions, and we consider a prediction to be correct only if every token of the model output matches the corresponding token in the ground truth.

BLEU BIBREF19: The metric evaluates the quality of the final response generated by natural language generator. The metric is usually used to measure the performance of the task-oriented dialogue system. 
 Question: What metrics are used to measure performance of models?

A:
BPRA APRA BLEU
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