
 The dataset for both tasks is a combination of the ArSarcasm \cite{abu-farha-magdy-2020-arabic} and DAICT  \cite{abbes-etal-2020-daict}, we used a 30\% subset of the training data to get a dev set. Statistics on the resulting data are shown in Table \ref{tab:label}. % shows statistics for the resulting combined dataset. 
 \input{tables/data-table.tex} 

%TODO: here group figures

  The distribution of labels (sarcasm and sentiment label) is rather imbalanced.
  In subtask-1, sarcastic tweets represent only 17\% of the data (1666 sarcastic VS 7883 non-sarcastic). % For example, this dataset contains 7883 not sarcastic tweet (83\%) vs 1666 sarcastic tweet as shown the .
  In subtask-2, positive tweets only represent 18\% of the data and negative tweets  represent no more than 37\%. %and neutral tweets respectively represent 37\% and 45\%.
 Hereafter we present some examples of labelled tweets.
  
%%% \begin{figure}[ht]
%%%  \centering 
%%%  \includegraphics[width=8cm]{figures/statistic_train_dev_data_sarcasm.png}
%%%  \caption{\label{fig1} Class distribution on the sarcasm train/dev data}
%%%\end{figure}
%%%The figure \ref{fig2} show the imbalanced class distribution of the sentiment train data. 
%%% 
%%% \begin{figure}[ht]
%%%     \centering
%%%     \includegraphics[width=8cm]{figures/statistic_train_dev_data_sentiment.png}
%%%     \caption{\label{fig2} Class distribution on the sentiment detection train/dev data}
%%% \end{figure}
 
 %The following are a few examples of sarcastic tweets(Example1) and Postitive,Negative, Neutre tweets respectively Example2, Example3 and Example4:

 
 \textbf{Sarcastic} : \textAR
 {بيقولك أخر مره فاز فيها الزمالك علي الأهلي كانت أيام ما كان الحكم بيصفر بصباعه 
} (They say that Zamalak's last victory against Al-Ahly happened when the referees had to use their fingers to whistle). %Ils disent que, la dernière victoire de Zamalak contre Al-Ahly était l'époque ou l'arbitre sifflait avec ses doigts. 
%They say, Zamalak's last victory against Al-Ahly was the time when the referee whistled with his fingers.

\textbf{Positive} : \textAR{ طلعنا من حلب الشرقية بسلام والحمدلله
} (We came out of East Aleppo in peace, thank God)
%Nous sommes sortis de d'Alep-Est en paix, merci Dieu. 

\textbf{Negative}:\textAR{ لبنان: ’’سخرية’’ من تعيين رجل وزيرا لشؤون المرأة
}. (Lebanon: "mocking" a male nominated as women's affairs minister)

%Liban: "Moquerie" lors de la nomination d'un homme ministre des affaires de la femme.


\textbf{Neutral} :\textAR{ في حدا نازل على حلب في ورق ضروري لازم ابعته و شكرا
 }. (Is there anyone going to Aleppo, I have important papers to send there. Thank you)
 %Y a t-il quelqu'un qui va à Alep, j'ai des papiers importants que je dois les envoyer et merci. 
 
 
