  Since their inception, transformer-based language models have led to
impressive performance gains across multiple natural language processing tasks.
For Arabic, the current state-of-the-art results on most datasets are achieved
by 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 dialectal
variations. This paper proffers team SPPU-AASM's submission for the WANLP
ArSarcasm shared-task 2021, which centers around the sarcasm and sentiment
polarity detection of Arabic tweets. The study proposes a hybrid model,
combining sentence representations from AraBERT with static word vectors
trained on Arabic social media corpora. The proposed system achieves a
F1-sarcastic score of 0.62 and a F-PN score of 0.715 for the sarcasm and
sentiment detection tasks, respectively. Simulation results show that the
proposed system outperforms multiple existing approaches for both the tasks,
suggesting that the amalgamation of context-free and context-dependent text
representations can help capture complementary facets of word meaning in
Arabic. The system ranked second and tenth in the respective sub-tasks of
sarcasm detection and sentiment identification.
