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Since their inception, transformer-based language models have led toimpressive performance gains across multiple natural language processing tasks.For Arabic, the current state-of-the-art results on most datasets are achievedby the AraBERT language model. Notwithstanding these recent advancements,sarcasm and sentiment detection persist to be challenging tasks in Arabic,given the language's rich morphology, linguistic disparity and dialectalvariations. This paper proffers team SPPU-AASM's submission for the WANLPArSarcasm shared-task 2021, which centers around the sarcasm and sentimentpolarity detection of Arabic tweets. The study proposes a hybrid model,combining sentence representations from AraBERT with static word vectorstrained on Arabic social media corpora. The proposed system achieves aF1-sarcastic score of 0.62 and a F-PN score of 0.715 for the sarcasm andsentiment detection tasks, respectively. Simulation results show that theproposed system outperforms multiple existing approaches for both the tasks,suggesting that the amalgamation of context-free and context-dependent textrepresentations can help capture complementary facets of word meaning inArabic. The system ranked second and tenth in the respective sub-tasks ofsarcasm detection and sentiment identification.

Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identification