Irony Detection in a Multilingual Context
Table 2: Results of the monolingual experiments (in percentage) in terms of accuracy (A), precision (P), recall (R), and macro F-score (F).
['Arabic', 'Arabic A', 'Arabic P', 'Arabic R', 'Arabic F', 'French A', 'French P', 'French R', 'French F', 'English A', 'English P', 'English R', 'English F']
Results. For English, our results, in terms of macro F-score (F), were not comparable to those of [Techc:2014, tay-etal-2018-reasoning], as we used 11% of the original dataset. For French, our scores are in line with those reported in state of the art (cf. best system in the irony shared task achieved F=78.3 [Deft2017]). They outperform those obtained for Arabic (A=71.7) [KarouiACLING:2017] and are comparable to those recently reported in the irony detection shared task in Arabic tweets [idat2019, ghanem2019idat] (F=84.4). Overall, the results show that semantic-based information captured by the embedding space are more productive comparing to standard surface and lexicon-based features.