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]: Analogous to word embeddings, sentence embeddings, e.g. the Universal Sentence Encoder BIBREF8 and Sentence-BERT BIBREF6, allow one to calculate the cosine similarity of various different sentences, as for instance the similarity of a question and the corresponding answer. The more appropriate a specific answer is to a given question, the stronger is its cosine similarity expected to be. When considering two opposite answers, it is therefore possible to determine a bias value:

where $\vec{q}$ is the vector representation of the question and $\vec{a}$ and $\vec{b}$ the representations of the two answers/choices. 
 Question: How is moral bias measured?
[A]: Abstractive


[Q]: Hashtag prediction for social media has been addressed earlier, for example in BIBREF15 , BIBREF16 . BIBREF15 also use a neural architecture, but compose text embeddings from a lookup table of words. 
 Question: Is this hashtag prediction task an established task, or something new?
[A]: Abstractive


[Q]: We carried out our experiments on the Universal Dependencies v1.2 treebanks BIBREF21 , hereafter UD1.2, from which morphosyntactically annotated corpora can be trivially extracted.  We considered UD1.2 corpora for the following 16 languages: Bulgarian, Croatian, Czech, Danish, English, French, German, Indonesian, Italian, Norwegian, Persian, Polish, Portuguese, Slovenian, Spanish and Swedish.  
 Question: which datasets did they experiment with?
[A]:
Abstractive