In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Ex 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”).

Ex Output:
What are the distinctive characteristics of how Arabic speakers use offensive language?


Ex Input:
We conduct various experiments to illustrate the properties that are encouraged via different KL magnitudes. In particular, we revisit the interdependence between rate and distortion, and shed light on the impact of KL on the sharpness of the approximated posteriors. Then, through a set of qualitative and quantitative experiments for text generation, we demonstrate how certain generative behaviours could be imposed on VAEs via a range of maximum channel capacities. Finally, we run some experiments to find if any form of syntactic information is encoded in the latent space.

Ex Output:
What different properties of the posterior distribution are explored in the paper?


Ex Input:
Table TABREF23 shows the Pearson, Spearman and Kendall correlation of Rouge and Sera, with pyramid scores. Interestingly, we observe that many variants of Rouge scores do not have high correlations with human pyramid scores. The lowest F-score correlations are for Rouge-1 and Rouge-L (with INLINEFORM0 =0.454). Weak correlation of Rouge-1 shows that matching unigrams between the candidate summary and gold summaries is not accurate in quantifying the quality of the summary.

Ex Output:

What different correlations result when using different variants of ROUGE scores?