 The purpose of this method is to see how much simple baselines can be competitive for such tasks. In particular, it seemed important to see how much important is tokenization in NLP pipelines.
  We compared word-level representations and character-level representations with various classifiers (Multinomial Naive Bayes, Decision trees,  Random Forest, Logistic regressions and SVM).
  It appeared that, except for the MNB classifier, word-based models only outperforms character-based models when the n-gram size is too small (using only 1-grams for instance) or way too big (using only N-grams with $N>8$ for instance).
  It is not surprising since, without lemmatization, words are just a subset of all the character n-grams of a text.
This observation is coherent with previous work showing the interest of character-level models for various NLP tasks \cite{nakov-tiedemann-2012-combining, kuru-etal-2016-charner, Buscaldi-2018}.

 Due to the restricted space we will just exhibit the results for the best classifier which was Logistic Regression. Figure \ref{char_size} exhibits the influence of N-gram size on the results for both tasks. %The horizontal dotted line represents the best result with word-level features.
 We can make two comments out of this figure : (i) unigrams are useful for classification even if they are not the best features by themselves and (ii) there is a plateau when the maximal size is set to 5, afterwards the results do not improve much (and even drop for the sarcasm detection task).
 This result leaded us to choose n-grams from 1 to 5 for our ensemble method (Section \ref{sec:run1}).
 This method was supposed to act as a baseline but finally, using tf-idf and weighting the classes made this baseline even better that our two other methods.
 \begin{figure}
   %\includegraphics[width=1\linewidth]{figures/Two_tasks_char-size.png}
%   \centering
\begin{center}
   \includegraphics[width=8.5cm]{figures/Two_tasks_char-size_mini_var.png}
 \end{center}
   \caption{Results on subtask 1 (sarc., lower curves) and subtask2 (sent., upper curves) wrt. minimal size (min) and maximal size (abscissa) of character n-grams\label{char_size}, the horizontal lines represent the best baseline with word uni-grams}% (Logistic Regression classifier)}
 \end{figure}
