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.
Irony in the Arabic Language
Detecting irony in Arabic tweets poses a significant challenge, as Arabic tweets are often characterized by :
- (a) non-diacritised texts,
- (b) a large variations of unstandardized dialectal Arabic, and
- (c) linguistic code switching between Modern Standard Arabic and several dialects and between Arabic and other languages like English and French.
The following two tweets illustrate this.
In tweet (1), the author ironically employs several positive opinion words ( ”character of the year”, ”perfect”) towards Morsi, the former president of Egypt to express a clearly negative opinion. The tweet (2) is also negative towards Salim Cheboub, daughter’s husband of the former Tunsian president Ben Ali, although there are any negative opinion words. The author here describes an ironic situation due to a sudden change of topic (Ali Ben Salem’s Uncle vs. Salim Cheboub) and a false assertion (”Salim Cheboub says that he has no relationship with Ben Ali” is not true in reality).
This task will be a good opportunity to compare the performances of Arabic irony detection to those reported in recent shared tasks in other languages like English (Van Hee et al, 2018), French (Benamara et al, 2017), and Italian (Cignarella et al, 2018).
The target audience are researchers from public entities (Universities, Research Centers) or private companies who are interested in figurative language processing or enhancing the performances of other NLP applications like sentiment analysis, hate speech detection or author profiling.
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