% This method uses the deep neural network method developed for the 2020 NADI challenge\cite{ghoul:hal-03089957}.
To build this system we used the \textsc{Aravec} pre-trained embeddings proposed by \textit{Soliman et al}. \textsc{Aravec} is composed of three models trained trained with Word2Vec skip-gram and CBOW \cite{mikolov2013efficient} on three different types of textual data in Arabic: tweets, web pages and Wikipedia articles \cite{SOLIMAN2017256}.
We used the 100-dimensional Twitter N-Grams model since it seemed to better fit to this task.

 The network is composed as follows: the input layer, an embedding layer, two LSTM layers and two Dense layers. To prevent over-fitting we add a dropout layer after each LSTM layer and the first dense layer.  We use the pre-mentioned \textsc{AraVec} embeddings to enrich the representation.  
 The final output is passed into one hidden layer and followed by a softmax output layer. There have been 10 epochs for the training. %TODO : reformlate last sentence
%To train this model, we used the train corpus with 10 epochs and evaluate it with the development corpus. 
%and. The results of sarcasm detection are reported in Table \ref{tab2} and of sentiement analysis in Table \ref{tab3}. %We can see that, contrary to Run1, this model suffers from over-fitting when trained on both train and dev set.
