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: The task is set up to mimic (albeit, in an oversimplified manner) the input-output symbol alignments and local syntactic properties that models must learn in many natural language tasks, such as translation, tagging and summarization. 
 Question: Why does the proposed task a good proxy for the general-purpose sequence to sequence tasks?

A: The task is set up to mimic (albeit, in an oversimplified manner) the input-output symbol alignments and local syntactic properties that models must learn in many natural language tasks, such as translation, tagging and summarization.
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Q: SIGHAN Bakeoff defines two types of evaluation settings, closed test limits all the data for learning should not be beyond the given training set, while open test does not take this limitation BIBREF21. 
 Question: What is meant by closed test setting?

A: closed test limits all the data for learning should not be beyond the given training set, while open test does not take this limitation
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Q: We build a dataset of Twitter accounts based on two lists annotated in previous works. For the non-factual accounts, we rely on a list of 180 Twitter accounts from BIBREF1. On the other hand, for the factual accounts, we use a list with another 32 Twitter accounts from BIBREF19 that are considered trustworthy by independent third parties. 
 Question: How did they obtain the dataset?

A:
For the non-factual accounts, we rely on a list of 180 Twitter accounts from BIBREF1 we use a list with another 32 Twitter accounts from BIBREF19 that are considered trustworthy
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