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.

[EX Q]: Both the audio and the transcript are de-identified (by removing the identifying information) with digital zeros and [de-identified] tags, respectively.
Question: Is the data de-identified?
[EX A]: Yes

[EX Q]: In our experiments we use the same data-sets from other works BIBREF32, BIBREF23, BIBREF33 as well as other datasets that we collected by us using a similar criterion (described in Section SECREF4). We use thirty different discussions that took place between March 2015 and June 2019, half of them with controversy and half without it. We considered discussions in four different languages: English, Portuguese, Spanish and French, occurring in five regions over the world: South and North America, Western Europe, Central and Southern Asia. We also studied these discussions taking first 140 characters and then 280 from each tweet to analyze the difference in performance and computing time wrt the length of the posts.
Question: What datasets did they use?
[EX A]: BIBREF32, BIBREF23, BIBREF33 discussions in four different languages: English, Portuguese, Spanish and French, occurring in five regions over the world: South and North America, Western Europe, Central and Southern Asia.

[EX Q]: The D2V model has been rated 80 times as "bad relevance" while the pmra returned only 24 times badly relevant documents.
Question: How better are results for pmra algorithm than Doc2Vec in human evaluation?
[EX A]:
The D2V model has been rated 80 times as "bad relevance" while the pmra returned only 24 times badly relevant documents.