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• W3156052545 abstract "The prominence of figurative language devices, such as sarcasm and irony, poses serious challenges for Arabic Sentiment Analysis (SA). While previous research works tackle SA and sarcasm detection separately, this paper introduces an end-to-end deep Multi-Task Learning (MTL) model, allowing knowledge interaction between the two tasks. Our MTL model’s architecture consists of a Bidirectional Encoder Representation from Transformers (BERT) model, a multi-task attention interaction module, and two task classifiers. The overall obtained results show that our proposed model outperforms its single-task and MTL counterparts on both sarcasm and sentiment detection subtasks." @default.
• W3156052545 created "2021-04-26" @default.
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• W3156052545 date "2021-04-01" @default.
• W3156052545 modified "2023-09-23" @default.
• W3156052545 title "Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language." @default.
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