Irony Detection in a Multilingual Context
Table 3: Results of the cross-lingual experiments.
['Train→Test', 'CNN A', 'CNN P', 'CNN R', 'CNN F', 'RF A', 'RF P', 'RF R', 'RF F']
We use the previous CNN architecture with bilingual embedding and the RF model with surface features (e.g., use of personal pronoun, presence of interjections, emoticon or specific punctuation) : (a) has similar ironic pragmatic devices, and (b) uses similar text-based pattern in the narrative of the ironic tweets. As continuous word embedding spaces exhibit similar structures across (even distant) languages [Mikolov:2013], we use a multilingual word representation which aims to learn a linear mapping from a source to a target embedding space. Many methods have been proposed to learn this mapping such as parallel data supervision and bilingual dictionaries [Mikolov:2013] or unsupervised methods relying on monolingual corpora [Conneau:2017, Artetxe:2018, Wada:2018]. For our experiments, we use Conneau et al ’s approach as it showed superior results with respect to the literature [Conneau:2017]. We perform several experiments by training on one language (lang1) and testing on another one (lang2) ( henceforth lang1→lang2). We get 6 configurations, plus two others to evaluate how irony devices are expressed cross-culturally, i.e. in European vs. non European languages. In each experiment, we took 20% from the training to validate the model before the testing process.