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.

In particular, we aggregate documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions. We name this dataset STORIES since most of the constituent documents take the form of a story with long chain of coherent events. Figure 5 -left and middle show that STORIES always yield the highest accuracy for both types of input processing. 
 Question: Which of their training domains improves performance the most?
documents from the CommonCrawl dataset that has the most overlapping n-grams with the question

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 manually reviewed 1,177 pairs of entities and referring expressions generated by the system. Overall, 73.3% of the non-first mentions of entities were replaced with suitable shorter and more fluent expressions. 
 Question: How is fluency of generated text evaluated?

manually reviewed

