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.

Input: Consider Input: We consider pre-training on the audio data (without labels) of WSJ, part of clean Librispeech (about 80h) and full Librispeech as well as a combination of all datasets (§ SECREF7 ).  Our experimental results on the WSJ benchmark demonstrate that pre-trained representations estimated on about 1,000 hours of unlabeled speech can substantially improve a character-based ASR system and outperform the best character-based result in the literature, Deep Speech 2.  
 Question: Which unlabeled data do they pretrain with?

Output: Abstractive


Input: Consider Input: For the input, we build a parallel monolingual corpus by translating the mixed language sequence using Google NMT to English ( INLINEFORM0 ) and Mandarin ( INLINEFORM1 ) sequences. 
 Question: What parallel corpus did they use?

Output: Abstractive


Input: Consider Input: Given the annotated tweets, we wanted to ascertain the distribution of: types of offensive language, genres where it is used, the dialects used, and the gender of users using such language. As the figure shows, sports and politics are most dominant for offensive language including vulgar and hate speech. As for dialect, we looked at MSA and four major dialects, namely Egyptian (EGY), Leventine (LEV), Maghrebi (MGR), and Gulf (GLF). Figure FIGREF14 shows that 71% of vulgar tweets were written in EGY followed by GLF, which accounted for 13% of vulgar tweets. MSA was not used in any of the vulgar tweets. As for offensive tweets in general, EGY and GLF were used in 36% and 35% of the offensive tweets respectively. Unlike the case of vulgar language where MSA was non-existent, 15% of the offensive tweets were in fact written in MSA. For hate speech, GLF and EGY were again dominant and MSA consistuted 21% of the tweets. This is consistent with findings for other languages such as English and Italian where vulgar language was more frequently associated with colloquial language BIBREF24, BIBREF25. Regarding the gender, Figure FIGREF15 shows that the vast majority of offensive tweets, including vulgar and hate speech, were authored by males. Female Twitter users accounted for 14% of offensive tweets in general and 6% and 9% of vulgar and hate speech respectively. Figure FIGREF16 shows a detailed categorization of hate speech types, where the top three include insulting groups based on their political ideology, origin, and sport affiliation. Religious hate speech appeared in only 15% of all hate speech tweets. 
 Question: How did they analyze which topics, dialects and gender are most associated with tweets?


Output: Extractive