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: As a starting point, we used the DIP corpus BIBREF37 , a collection of 49 clusters of 100 web pages on educational topics (e.g. bullying, homeschooling, drugs) with a short description of each topic. 
 Question: Which collections of web documents are included in the corpus?

A: Extractive
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Q: We validate the performance of the proposed s2sL by providing the preliminary results obtained on two different tasks namely, Speech/Music discrimination and emotion classification. We considered the GTZAN Music-Speech dataset [17], consisting of 120 audio files (60 speech and 60 music), for task of classifying speech and music. Each audio file (of 2 seconds duration) is represented using a 13-dimensional mel-frequency cepstral coefficient (MFCC) vector, where each MFCC vector is the average of all the frame level (frame size of 30 msec and an overlap of 10 msec) MFCC vectors. It is to be noted that our main intention for this task is not better feature selection, but to demonstrate the effectiveness of our approach, in particular for low data scenarios. The standard Berlin speech emotion database (EMO-DB) [18] consisting of 535 utterances corresponding to 7 different emotions is considered for the task of emotion classification.  
 Question: Up to how many samples do they experiment with?

A: Extractive
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Q: Tweets IDs 6, 8, and 10 are some samples containing offensive words and slurs which arenot hate or offensive in all cases and writers of them used this type of language in their daily communications. Given these pieces of evidence, by considering the content of tweets, we can see in tweets IDs 3, 4, and 9 that our BERT-based classifier can discriminate tweets in which neither and implicit hatred content exist. 
 Question: What evidence do the authors present that the model can capture some biases in data annotation and collection?

A:
Abstractive
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