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]: For all tasks, we use a TensorFlow implementation. AraNet predicts age, dialect, gender, emotion, irony, and sentiment from social media posts. It delivers state-of-the-art and competitive performance on these tasks and has the advantage of using a unified, simple framework based on the recently-developed BERT model.  
 Question: Did they experiment on all the tasks?
[A]: Yes


[Q]: Which events authors choose to include in their history, which they leave out, and the way the events chosen relate to the march are crucial factors in conveying bias. 
 Question: What factors contribute to interpretive biases according to this research?
[A]: Which events authors choose to include in their history, which they leave out, and the way the events chosen relate to the march


[Q]: In contrast, in order to exploit section information, in this paper we propose to capture a distributed representation of both the global (the whole document) and the local context (e.g., the section/topic) when deciding if a sentence should be included in the summary 
 Question: What do they mean by global and local context?
[A]:
global (the whole document) local context (e.g., the section/topic)