In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

[Q]: TABREF5 displays the output behavior of models that we damaged to resemble the damage that causes aphasia. Because the output languages in all of our domains use tokens to represent meanings in many cases, it is expected that the analog to Wernicke's area is responsible for maintaining a high precision.
[A]: Do they perform a quantitative analysis of their model displaying knowledge distortions?

[Q]: Experimental results show that our joint model outperforms the visual-only model in all cases, and the text-only model on Wikipedia and two subsets of arXiv.
[A]: Do the methods that work best on academic papers also work best on Wikipedia?

[Q]: Tweets related to Forex, specifically to EUR and USD, were acquired through the Twitter search API with the following query: "EURUSD", "USDEUR", "EUR", or "USD". In the period of three years (January 2014 to December 2016) almost 15 million tweets were collected. A subset of them (44,000 tweets) was manually labeled by knowledgeable students of finance.
[A]:
How many tweets were manually labelled?