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: Next, we analyzed all tweets labeled as offensive to better understand how Arabic speakers use offensive language. Here is a breakdown of usage:

Direct name calling: The most frequent attack is to call a person an animal name, and the most used animals were كلب> (“klb” - “dog”), حمار> (“HmAr” - “donkey”), and بهيم> (“bhym” - “beast”). The second most common was insulting mental abilities using words such as غبي> (“gby” - “stupid”) and عبيط> (“EbyT” -“idiot”). Some culture-specific differences should be considered. Not all animal names are used as insults. For example, animals such as أسد> (“Asd” - “lion”), صقر> (“Sqr” - “falcon”), and غزال> (“gzAl” - “gazelle”) are typically used for praise. For other insults, people use: some bird names such as دجاجة> (“djAjp” - “chicken”), بومة> (“bwmp” - “owl”), and غراب> (“grAb” - “crow”); insects such as ذبابة> (“*bAbp” - “fly”), صرصور> (“SrSwr” - “cockroach”), and حشرة> (“H$rp” - “insect”); microorganisms such as جرثومة> (“jrvwmp” - “microbe”) and طحالب> (“THAlb” - “algae”); inanimate objects such as جزمة> (“jzmp” - “shoes”) and سطل> (“sTl” - “bucket”) among other usages.

Simile and metaphor: Users use simile and metaphor were they would compare a person to: an animal as in زي الثور> (“zy Alvwr” - “like a bull”), سمعني نهيقك> (“smEny nhyqk” - “let me hear your braying”), and هز ديلك> (“hz dylk” - “wag your tail”); a person with mental or physical disability such as منغولي> (“mngwly” - “Mongolian (down-syndrome)”), معوق> (“mEwq” - “disabled”), and قزم> (“qzm” - “dwarf”); and to the opposite gender such as جيش نوال> (“jy$ nwAl” - “Nawal's army (Nawal is female name)”) and نادي زيزي> (“nAdy zyzy” - “Zizi's club (Zizi is a female pet name)”).

Indirect speech: This type of offensive language includes: sarcasm such as أذكى إخواتك> (“A*kY AxwAtk” - “smartest one of your siblings”) and فيلسوف الحمير> (“fylswf AlHmyr” - “the donkeys' philosopher”); questions such as ايه كل الغباء ده> (“Ayh kl AlgbA dh” - “what is all this stupidity”); and indirect speech such as النقاش مع البهايم غير مثمر> (“AlnqA$ mE AlbhAym gyr mvmr” - “no use talking to cattle”).

Wishing Evil: This entails wishing death or major harm to befall someone such as ربنا ياخدك> (“rbnA yAxdk” - “May God take (kill) you”), الله يلعنك> (“Allh ylEnk” - “may Allah/God curse you”), and روح في داهية> (“rwH fy dAhyp” - equivalent to “go to hell”).

Name alteration: One common way to insult others is to change a letter or two in their names to produce new offensive words that rhyme with the original names. Some examples of such include changing الجزيرة> (“Aljzyrp” - “Aljazeera (channel)”) to الخنزيرة> (“Alxnzyrp” - “the pig”) and خلفان> (“xlfAn” - “Khalfan (person name)”) to خرفان> (“xrfAn” - “crazed”).

Societal stratification: Some insults are associated with: certain jobs such as بواب> (“bwAb” - “doorman”) or خادم> (“xAdm” - “servant”); and specific societal components such بدوي> (“bdwy” - “bedouin”) and فلاح> (“flAH” - “farmer”).

Immoral behavior: These insults are associated with negative moral traits or behaviors such as حقير> (“Hqyr” - “vile”), خاين> (“xAyn” - “traitor”), and منافق> (“mnAfq” - “hypocrite”).

Sexually related: They include expressions such as خول> (“xwl” - “gay”), وسخة> (“wsxp” - “prostitute”), and عرص> (“ErS” - “pimp”). 
 Question: What are the distinctive characteristics of how Arabic speakers use offensive language?
Output: 


Answer: Abstractive