Definition: 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.

Positive Example 1 -
Input: We build a dataset of Twitter accounts based on two lists annotated in previous works. For the non-factual accounts, we rely on a list of 180 Twitter accounts from BIBREF1. On the other hand, for the factual accounts, we use a list with another 32 Twitter accounts from BIBREF19 that are considered trustworthy by independent third parties. 
 Question: How did they obtain the dataset?
Output: Extractive

Positive Example 2 -
Input: We evaluate our pointer-generator performance using BLEU score. The baseline language model is trained using RNNLM BIBREF23 . Perplexity measure is used in the evaluation.

 
 Question: Did they use other evaluation metrics?
Output: Yes-no

Negative Example 1 -
Input: For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains  For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains  The evaluation of the German version is in progress. 
 Question: What are the corpora used for the task?
Output: Yes-no

Negative Example 2 -
Input: For Turkish, as the first dataset, we utilised the movie reviews which are collected from a popular website. The number of reviews in this movie corpus is 20,244 and the average number of words in reviews is 39. Each of these reviews has a star-rating score which is indicative of sentiment. These polarity scores are between the values 0.5 and 5, at intervals of 0.5. We consider a review to be negative it the score is equal to or lower than 2.5. On the other hand, if it is equal to or higher than 4, it is assumed to be positive. We have randomly selected 7,020 negative and 7,020 positive reviews and processed only them. 
 Question: What details are given about the movie domain dataset?
Output: Extractive

Now complete the following example -
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: 


Answer: Extractive