[EN] This overview paper describes the first shared task on irony
detection for the Arabic language. The task consists of a binary classification of tweets as ironic or not using a dataset composed of 5,030
Arabic tweets about different political issues and events related to the
Middle East and the Maghreb. Tweets in our dataset are written in
Modern Standard Arabic but also in different Arabic language varieties
including Egypt, Gulf, Levantine and Maghrebi dialects. Eighteen teams
registered to the task among which ten submitted their runs. The methods of participants ranged from feature-based to neural networks using
either classical machine learning techniques or ensemble methods. The
best performing system achieved F-score value of 0.844, showing that
classical feature-based models outperform the neural ones.This publication was made possible by NPRP grant 9-175-1-033 from the Qatar
National Research Fund (a member of Qatar Foundation). The findings achieved
herein are solely the responsibility of the last author. The work of Paolo Rosso
was also partially funded by Generalitat Valenciana under grant PROMETEO/2019/121.Ghanem, B.; Karoui, J.; Benamara, F.; Moriceau, V.; Rosso, P. (2019). IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets. CEUR-WS.org. 380-390. http://hdl.handle.net/10251/180744S38039