In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.

Q: We focus on social media posts from the website Twitter, which are an excellent testing ground for character based models due to the noisy nature of text. Heavy use of slang and abundant misspellings means that there are many orthographically and semantically similar tokens, and special characters such as emojis are also immensely popular and carry useful semantic information. In our moderately sized training dataset of 2 million tweets, there were about 0.92 million unique word types. 
 Question: Does the paper clearly establish that the challenges listed here exist in this dataset and task?

A: Yes-no
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Q: Second, we compute three metrics on the extracted information:

$\bullet $ Relation Generation (RG) estimates how well the system is able to generate text containing factual (i.e., correct) records. We measure the precision and absolute number (denoted respectively RG-P% and RG-#) of unique relations $r$ extracted from $\hat{y}_{1:T}$ that also appear in $s$.

$\bullet $ Content Selection (CS) measures how well the generated document matches the gold document in terms of mentioned records. We measure the precision and recall (denoted respectively CS-P% and CS-R%) of unique relations $r$ extracted from $\hat{y}_{1:T}$ that are also extracted from $y_{1:T}$.

$\bullet $ Content Ordering (CO) analyzes how well the system orders the records discussed in the description. We measure the normalized Damerau-Levenshtein distance BIBREF36 between the sequences of records extracted from $\hat{y}_{1:T}$ that are also extracted from $y_{1:T}$. 
 Question: Which qualitative metric are used for evaluation?

A: Extractive
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Q: Throughout this article, we use two different embedding spaces. The first is the widely used representation built on GoogleNews BIBREF8 . The second is taken from BIBREF2 , and was trained on a Reddit dataset BIBREF9 . 
 Question: Which embeddings do they detect biases in?

A:
Abstractive
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