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]: We build on the state-of-the-art publicly available question answering system by docqa. The system extends BiDAF BIBREF4 with self-attention and performs well on document-level QA.  
 Question: What is the underlying question answering algorithm?
[A]: The system extends BiDAF BIBREF4 with self-attention


[Q]: We then show the tweet text and image and we ask them to classify it in one of 6 categories: No attacks to any community, racist, sexist, homophobic, religion based attacks or attacks to other communities. 
 Question: What annotations are available in the dataset - tweat used hate speach or not?
[A]: No attacks to any community  racist sexist homophobic religion based attacks attacks to other communities


[Q]: In the instances where a model thinks all historical samples should be considered equally important in a sequential analysis task, we must look elsewhere for a computationally inexpensive means to understand what happened at the stopping point. 
 Question: Can their method of creating more informative visuals be applied to tasks other than turn taking in conversations?
[A]:
computationally inexpensive means to understand what happened at the stopping point