!Qué maravilla! Multimodal Sarcasm Detection in Spanish: a Dataset and a Baseline
This addresses sarcasm detection for Spanish speakers, but it is incremental as it extends existing multimodal methods to a new language.
The authors tackled sarcasm detection in Spanish by creating the first multimodal dataset with text, audio, and video, and found that combining all modalities achieved the best accuracy of 93.1%.
We construct the first ever multimodal sarcasm dataset for Spanish. The audiovisual dataset consists of sarcasm annotated text that is aligned with video and audio. The dataset represents two varieties of Spanish, a Latin American variety and a Peninsular Spanish variety, which ensures a wider dialectal coverage for this global language. We present several models for sarcasm detection that will serve as baselines in the future research. Our results show that results with text only (89%) are worse than when combining text with audio (91.9%). Finally, the best results are obtained when combining all the modalities: text, audio and video (93.1%).