Irony is a complex linguistic phenomenon widely studied in philosophy and linguistics. Irony can be defined as an incongruity between the literal meaning of an utterance and its intended meaning, that is meaning that is not strictly the conventional meaning of the individual words in the figurative expression. Irony overlaps with a variety of other figurative devices such as satire, parody, and sarcasm (Clark and Gerrig 1984; Gibbs 2000). In this task, irony is used as an umbrella term that includes sarcasm.
Irony detection has gained relevance recently, due to its importance in various NLP applications such as sentiment analysis, hate speech detection, author profiling, fake news detection, and crisis management (e.g., terrorist attacks, public disorder). For example, recent studies on irony show that the performances of sentiment analysis systems drastically decrease when applied to ironic texts (Benamara et al, 2017, Hernandez Faŕıas et al., 2016, 2018, Zhang et al, 2019). This is mainly due to the complexity of ironic contents that make use of figures of speech to convey non-literal meaning.
The task aims at detecting irony in Arabic tweets. Given a tweet, systems have to classify it as either ironic or not ironic. As far as we know, this is the first shared task on irony for the Arabic language.