The paper describes SemEval-2022’s shared task “Intended Sarcasm Detection in English and Arabic.” This task includes English and Arabic tweets with sarcasm and non-sarcasm samples and irony speech labels.The first two subtasks predict whether a text is sarcastic and the ironic category the sarcasm sample belongs to. The third one is to find the sarcastic sample from its non-sarcastic paraphrase. Deep neural networks have recently achieved highly competitive performance in many tasks.Combining deep learning with language models has also resulted in acceptable accuracy. Inspired by this, we propose a novel deep learning model on top of language models. On top of T5, this architecture uses an encoder module of the transformer, followed by LSTM and attention to utilizing past and future information, concentrating on informative tokens. Due to the success of the proposed model, we used the same architecture with a few modifications to the output layer in all three subtasks.

The multilingual complex named entity recognition task of SemEval2020 required participants to detect semantically ambiguous and complex entities in 11 languages. In order to participate in this competition, a deep learning model is being used with the T5 text-to-text language model and its multilingual version, MT5, along with the transformer’s encoder module. The subtoken check has also been introduced, resulting in a 4% increase in the model F1-score in English. We also examined the use of the BPEmb model for converting input tokens to representation vectors in this research. A performance evaluation of the proposed entity detection model is presented at the end of this paper. Six different scenarios were defined, and the proposed model was evaluated in each scenario within the English development set. Our model is also evaluated in other languages.